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17 pages, 3558 KiB

Open AccessArticle

Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries

by Hanan Abdullah Mengash, Nuha Alruwais, Fadoua Kouki, Chinu Singla, Elmouez Samir Abd Elhameed and Ahmed Mahmud

Biomimetics 2024, 9(1), 1; https://doi.org/10.3390/biomimetics9010001 - 19 Dec 2023

Cited by 6 | Viewed by 2592

Abstract

Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition [...] Read more.

Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveraging the capability of deep learning (DL) algorithms, the telecom companies can develop highly robust predictive models that can efficiently anticipate and mitigate customer churn by boosting retention approaches. In this background, the current study presents the Archimedes optimization algorithm-based feature selection with a hybrid deep-learning-based churn prediction (AOAFS-HDLCP) technique for telecom companies. In order to mitigate high-dimensionality problems, the AOAFS-HDLCP technique involves the AOAFS approach to optimally choose a set of features. In addition to this, the convolutional neural network with autoencoder (CNN-AE) model is also involved for the churn prediction process. Finally, the thermal equilibrium optimization (TEO) technique is employed for hyperparameter selection of the CNN-AE algorithm, which, in turn, helps in achieving improved classification performance. A widespread experimental analysis was conducted to illustrate the enhanced performance of the AOAFS-HDLCP algorithm. The experimental outcomes portray the high efficiency of the AOAFS-HDLCP approach over other techniques, with a maximum accuracy of 94.65%. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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23 pages, 6615 KiB

Open AccessArticle

A New Hyper-Heuristic Multi-Objective Optimisation Approach Based on MOEA/D Framework

by Jiayi Han and Shinya Watanabe

Biomimetics 2023, 8(7), 521; https://doi.org/10.3390/biomimetics8070521 - 2 Nov 2023

Cited by 3 | Viewed by 1788

Abstract

A multi-objective evolutionary algorithm based on decomposition (MOEA/D) serves as a robust framework for addressing multi-objective optimization problems (MOPs). However, it is widely recognized that the applicability of a fixed offspring-generating strategy in MOEA/D can be limited, despite its foundation in the MOEA/D [...] Read more.

A multi-objective evolutionary algorithm based on decomposition (MOEA/D) serves as a robust framework for addressing multi-objective optimization problems (MOPs). However, it is widely recognized that the applicability of a fixed offspring-generating strategy in MOEA/D can be limited, despite its foundation in the MOEA/D methodology. Consequently, hybrid algorithms have gained popularity in recent years. This study proposes a novel hyper-heuristic approach that integrates the estimation of distribution (ED) and crossover (CX) strategies into the MOEA/D framework based on the view of successful replacement rate (SSR) and attempts to explain the potential reasons for the advantages of hybrid algorithms. The proposed approach dynamically switches from the differential evolution (DE) operator to the covariance matrix adaptation evolution strategy (CMA-ES) operator. Simultaneously, certain subproblems in the neighbourhood denoted as

B (i)

employ the Improved Differential Evolution (IDE) operator to generate new individuals for balancing the high evaluation costs associated with CMA-ES. Numerical experiments unequivocally demonstrate that the suggested approach offers distinct advantages when applied to a three-objective test suite. These experiments also validate a significant enhancement in the efficiency (SRR) of the DE operator within this context. The perspectives and experimental findings, with a focus on the Success Rate Ratio (SRR), have the potential to provide valuable insights and inspire further research in related domains. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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27 pages, 5197 KiB

Open AccessArticle

An Advanced Bio-Inspired Mantis Search Algorithm for Characterization of PV Panel and Global Optimization of Its Model Parameters

by Ghareeb Moustafa, Hashim Alnami, Sultan Hassan Hakmi, Ahmed Ginidi, Abdullah M. Shaheen and Fahad A. Al-Mufadi

Biomimetics 2023, 8(6), 490; https://doi.org/10.3390/biomimetics8060490 - 18 Oct 2023

Cited by 20 | Viewed by 2419

Abstract

Correct modelling and estimation of solar cell characteristics are crucial for effective performance simulations of PV panels, necessitating the development of creative approaches to improve solar energy conversion. When handling this complex problem, traditional optimisation algorithms have significant disadvantages, including a predisposition to [...] Read more.

Correct modelling and estimation of solar cell characteristics are crucial for effective performance simulations of PV panels, necessitating the development of creative approaches to improve solar energy conversion. When handling this complex problem, traditional optimisation algorithms have significant disadvantages, including a predisposition to get trapped in certain local optima. This paper develops the Mantis Search Algorithm (MSA), which draws inspiration from the unique foraging behaviours and sexual cannibalism of praying mantises. The suggested MSA includes three stages of optimisation: prey pursuit, prey assault, and sexual cannibalism. It is created for the R.TC France PV cell and the Ultra 85-P PV panel related to Shell PowerMax for calculating PV parameters and examining six case studies utilising the one-diode model (1DM), two-diode model (1DM), and three-diode model (3DM). Its performance is assessed in contrast to recently developed optimisers of the neural network optimisation algorithm (NNA), dwarf mongoose optimisation (DMO), and zebra optimisation algorithm (ZOA). In light of the adopted MSA approach, simulation findings improve the electrical characteristics of solar power systems. The developed MSA methodology improves the 1DM, 2DM, and 3DM by 12.4%, 44.05%, and 48.88%, 28.96%, 43.19%, and 55.81%, 37.71%, 32.71%, and 60.13% relative to the DMO, NNA, and ZOA approaches, respectively. For the Ultra 85-P PV panel, the designed MSA technique achieves improvements for the 1DM, 2DM, and 3DM of 62.05%, 67.14%, and 84.25%, 49.05%, 53.57%, and 74.95%, 37.03%, 37.4%, and 59.57% compared to the DMO, NNA, and ZOA techniques, respectively. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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24 pages, 5744 KiB

Open AccessArticle

Implementation of Chaotic Reverse Slime Mould Algorithm Based on the Dandelion Optimizer

by Yi Zhang, Yang Liu, Yue Zhao and Xu Wang

Biomimetics 2023, 8(6), 482; https://doi.org/10.3390/biomimetics8060482 - 11 Oct 2023

Cited by 2 | Viewed by 1857

Abstract

This paper presents a hybrid algorithm based on the slime mould algorithm (SMA) and the mixed dandelion optimizer. The hybrid algorithm improves the convergence speed and prevents the algorithm from falling into the local optimal. (1) The Bernoulli chaotic mapping is added in [...] Read more.

This paper presents a hybrid algorithm based on the slime mould algorithm (SMA) and the mixed dandelion optimizer. The hybrid algorithm improves the convergence speed and prevents the algorithm from falling into the local optimal. (1) The Bernoulli chaotic mapping is added in the initialization phase to enrich the population diversity. (2) The Brownian motion and Lévy flight strategy are added to further enhance the global search ability and local exploitation performance of the slime mould. (3) The specular reflection learning is added in the late iteration to improve the population search ability and avoid falling into local optimality. The experimental results show that the convergence speed and precision of the improved algorithm are improved in the standard test functions. At last, this paper optimizes the parameters of the Extreme Learning Machine (ELM) model with the improved method and applies it to the power load forecasting problem. The effectiveness of the improved method in solving practical engineering problems is further verified. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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18 pages, 921 KiB

Open AccessArticle

A Reinforcement Learning Approach to Robust Scheduling of Permutation Flow Shop

by Tao Zhou, Liang Luo, Shengchen Ji and Yuanxin He

Biomimetics 2023, 8(6), 478; https://doi.org/10.3390/biomimetics8060478 - 7 Oct 2023

Cited by 3 | Viewed by 2036

Abstract

The permutation flow shop scheduling problem (PFSP) stands as a classic conundrum within the realm of combinatorial optimization, serving as a prevalent organizational structure in authentic production settings. Given that conventional scheduling approaches fall short of effectively addressing the intricate and ever-shifting production [...] Read more.

The permutation flow shop scheduling problem (PFSP) stands as a classic conundrum within the realm of combinatorial optimization, serving as a prevalent organizational structure in authentic production settings. Given that conventional scheduling approaches fall short of effectively addressing the intricate and ever-shifting production landscape of PFSP, this study proposes an end-to-end deep reinforcement learning methodology with the objective of minimizing the maximum completion time. To tackle PFSP, we initially model it as a Markov decision process, delineating pertinent states, actions, and reward functions. A notably innovative facet of our approach involves leveraging disjunctive graphs to represent PFSP state information. To glean the intrinsic topological data embedded within the disjunctive graph’s underpinning, we architect a policy network based on a graph isomorphism network, subsequently trained through proximal policy optimization. Our devised methodology is compared with six baseline methods on randomly generated instances and the Taillard benchmark, respectively. The experimental results unequivocally underscore the superiority of our proposed approach in terms of

m a k e s p a n

and computation time. Notably, the

m a k e s p a n

can save up to 183.2 h in randomly generated instances and 188.4 h in the Taillard benchmark. The calculation time can be reduced by up to 18.70 s for randomly generated instances and up to 18.16 s for the Taillard benchmark. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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18 pages, 11666 KiB

Open AccessArticle

Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images

by Rayed AlGhamdi

Biomimetics 2023, 8(6), 474; https://doi.org/10.3390/biomimetics8060474 - 5 Oct 2023

Cited by 2 | Viewed by 2165

Abstract

Histopathological grading of the tumors provides insights about the patient’s disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division [...] Read more.

Histopathological grading of the tumors provides insights about the patient’s disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division (mitosis) process or not. This is an essential procedure in several research and medical contexts, especially in diagnosis and prognosis of cancer. Mitotic nuclei classification is a challenging task since the size of the nuclei is too small to observe, while the mitotic figures possess a different appearance as well. Automated calculation of mitotic nuclei is a stimulating one due to their great similarity to non-mitotic nuclei and their heteromorphic appearance. Both Computer Vision (CV) and Machine Learning (ML) approaches are used in the automated identification and the categorization of mitotic nuclei in histopathological images that endure the procedure of cell division (mitosis). With this background, the current research article introduces the mitotic nuclei segmentation and classification using the chaotic butterfly optimization algorithm with deep learning (MNSC-CBOADL) technique. The main objective of the MNSC-CBOADL technique is to perform automated segmentation and the classification of the mitotic nuclei. In the presented MNSC-CBOADL technique, the U-Net model is initially applied for the purpose of segmentation. Additionally, the MNSC-CBOADL technique applies the Xception model for feature vector generation. For the classification process, the MNSC-CBOADL technique employs the deep belief network (DBN) algorithm. In order to enhance the detection performance of the DBN approach, the CBOA is designed for the hyperparameter tuning model. The proposed MNSC-CBOADL system was validated through simulation using the benchmark database. The extensive results confirmed the superior performance of the proposed MNSC-CBOADL system in the classification of mitotic nuclei. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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23 pages, 4841 KiB

Open AccessArticle

Vegetation Evolution with Dynamic Maturity Strategy and Diverse Mutation Strategy for Solving Optimization Problems

by Rui Zhong, Fei Peng, Enzhi Zhang, Jun Yu and Masaharu Munetomo

Biomimetics 2023, 8(6), 454; https://doi.org/10.3390/biomimetics8060454 - 25 Sep 2023

Cited by 9 | Viewed by 1526

Abstract

We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a higher probability of generating more seed [...] Read more.

We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a higher probability of generating more seed individuals. Here, all individuals will first become allocated to generate a fixed number of seeds, and then the remaining number of allocatable seeds will be distributed competitively according to their fitness. Since VEGE performs poorly in getting rid of local optima, we propose the diverse mutation strategy as the second search operator with several different mutation methods to increase the diversity of seed individuals. In other words, each generated seed individual will randomly choose one of the methods to mutate with a lower probability. To evaluate the performances of the two proposed strategies, we run our proposal (VEGE + two strategies), VEGE, and another seven advanced evolutionary algorithms (EAs) on the CEC2013 benchmark functions and seven popular engineering problems. Finally, we analyze the respective contributions of these two strategies to VEGE. The experimental and statistical results confirmed that our proposal can significantly accelerate convergence and improve the convergence accuracy of the conventional VEGE in most optimization problems. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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37 pages, 6731 KiB

Open AccessArticle

An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems

by Yaoyao Lin, Ali Asghar Heidari, Shuihua Wang, Huiling Chen and Yudong Zhang

Biomimetics 2023, 8(5), 441; https://doi.org/10.3390/biomimetics8050441 - 20 Sep 2023

Cited by 4 | Viewed by 3327

Abstract

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is [...] Read more.

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm’s exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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13 pages, 6103 KiB

Open AccessArticle

Fault Reconfiguration in Distribution Networks Based on Improved Discrete Multimodal Multi-Objective Particle Swarm Optimization Algorithm

by Xin Li, Mingyang Li, Moduo Yu and Qinqin Fan

Biomimetics 2023, 8(5), 431; https://doi.org/10.3390/biomimetics8050431 - 18 Sep 2023

Cited by 8 | Viewed by 1808

Abstract

Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault [...] Read more.

Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault reconfiguration of the distribution network is often regarded as a single-objective or multi-objective optimization problem, and its multimodality is often ignored in existing studies. Therefore, the obtained solutions may be unsuitable or infeasible when the environment changes. To improve the availability and robustness of the solutions, an improved discrete multimodal multi-objective particle swarm optimization (IDMMPSO) algorithm is proposed to solve the fault reconfiguration problem of the distribution network. To demonstrate the performance of the proposed IDMMPSO algorithm, the IEEE33-bus distribution system is used in the experiment. Moreover, the proposed algorithm is compared with other competitors. Experimental results show that the proposed algorithm can provide different equivalent solutions for decision-makers in solving the fault reconfiguration problem of the distribution network. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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23 pages, 47149 KiB

Open AccessArticle

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain’s Cognitive Mechanism

by Yishen Liao, Naigong Yu and Jinhan Yan

Biomimetics 2023, 8(5), 427; https://doi.org/10.3390/biomimetics8050427 - 14 Sep 2023

Viewed by 1774

Abstract

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat’s brain. Based on these, this paper proposes [...] Read more.

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat’s brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain’s cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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16 pages, 2793 KiB

Open AccessArticle

Dynamic System Stability Modeling Approach with Sparrow-Inspired Meta-Heuristic Optimization Algorithm

by Tianqi Xia, Mingming Zhang and Shaohong Wang

Biomimetics 2023, 8(5), 424; https://doi.org/10.3390/biomimetics8050424 - 13 Sep 2023

Cited by 1 | Viewed by 1598

Abstract

Aiming at the accurate prediction of the inception of instability in a compressor, a dynamic system stability model is proposed based on a sparrow-inspired meta-heuristic optimization algorithm in this article. To achieve this goal, a spatial mode is employed for flow field feature [...] Read more.

Aiming at the accurate prediction of the inception of instability in a compressor, a dynamic system stability model is proposed based on a sparrow-inspired meta-heuristic optimization algorithm in this article. To achieve this goal, a spatial mode is employed for flow field feature extraction and modeling object acquisition. The nonlinear characteristic presented in the system is addressed using fuzzy entropy as the identification strategy to provide a basis for instability determination. Using Sparrow Search Algorithm (SSA) optimization, a Radial Basis Function Neural Network (RBFNN) is achieved for the performance prediction of system status. A Logistic SSA solution is first established to seek the optimal parameters of the RBFNN to enhance prediction accuracy and stability. On the basis of the RBFNN-LSSA hybrid model, the stall inception is detected about 35.8 revolutions in advance using fuzzy entropy identification. To further improve the multi-step network model, a Tent SSA is introduced to promote the accuracy and robustness of the model. A wider range of potential solutions within the TSSA are explored by incorporating the Tent mapping function. The TSSA-based optimization method proves a suitable adaptation for complex nonlinear dynamic modeling. And this method demonstrates superior performance, achieving 42 revolutions of advance warning with multi-step prediction. This RBFNN-TSSA model represents a novel and promising approach to the application of system modeling. These findings contribute to enhancing the abnormal warning capability of dynamic systems in compressors. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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20 pages, 4357 KiB

Open AccessArticle

Modeling the Energy Consumption of R600a Gas in a Refrigeration System with New Explainable Artificial Intelligence Methods Based on Hybrid Optimization

by Sinem Akyol, Mehmet Das and Bilal Alatas

Biomimetics 2023, 8(5), 397; https://doi.org/10.3390/biomimetics8050397 - 30 Aug 2023

Cited by 2 | Viewed by 3320

Abstract

Refrigerant gases, an essential cooling system component, are used in different processes according to their thermophysical properties and energy consumption values. The low global warming potential and energy consumption values of refrigerant gases are primarily preferred in terms of use. Recently, studies on [...] Read more.

Refrigerant gases, an essential cooling system component, are used in different processes according to their thermophysical properties and energy consumption values. The low global warming potential and energy consumption values of refrigerant gases are primarily preferred in terms of use. Recently, studies on modeling properties such as compressor energy consumption, efficiency coefficient, exergy, and thermophysical properties of refrigerants in refrigeration systems with artificial intelligence methods has become increasingly common. In this study, a hybrid-optimization-based artificial intelligence classification method is applied for the first time to produce explainable, interpretable, and transparent models of compressor energy consumption in a vapor compression refrigeration system operating with R600a refrigerant gas. This methodological innovation obtains models that determine the energy consumption values of R600a gas according to the operating parameters. From these models, the operating conditions with the lowest energy consumption are automatically revealed. The innovative artificial intelligence method applied for the energy consumption value determines the system’s energy consumption according to the operating temperatures and pressures of the evaporator and condenser unit. When the obtained energy consumption model results were compared with the experimental results, it was seen that it had an accuracy of 84.4%. From this explainable artificial intelligence method, which is applied for the first time in the field of refrigerant gas, the most suitable operating conditions that can be achieved based on the minimum, medium, and maximum energy consumption ranges of different refrigerant gases can be determined. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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30 pages, 3786 KiB

Open AccessArticle

Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation

by Hongmin Chen, Zhuo Wang, Heming Jia, Xindong Zhou and Laith Abualigah

Biomimetics 2023, 8(5), 396; https://doi.org/10.3390/biomimetics8050396 - 28 Aug 2023

Cited by 11 | Viewed by 2271

Abstract

The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult [...] Read more.

The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult to find the optimal position in complex functions. The arithmetic optimization algorithm utilizes multiplication and division operators for position updates, which have strong randomness and good convergence ability. For the above, this paper integrates the two algorithms and adds a random central solution strategy, a mutation strategy, and a restart strategy. A hybrid slime mold and arithmetic optimization algorithm with random center learning and restart mutation (RCLSMAOA) is proposed. The improved algorithm retains the position update formula of the slime mold algorithm in the global exploration section. It replaces the convergence stage of the slime mold algorithm with the multiplication and division algorithm in the local exploitation stage. At the same time, the stochastic center learning strategy is adopted to improve the global search efficiency and the diversity of the algorithm population. In addition, the restart strategy and mutation strategy are also used to improve the convergence accuracy of the algorithm and enhance the later optimization ability. In comparison experiments, different kinds of test functions are used to test the specific performance of the improvement algorithm. We determine the final performance of the algorithm by analyzing experimental data and convergence images, using the Wilcoxon rank sum test and Friedman test. The experimental results show that the improvement algorithm, which combines the slime mold algorithm and arithmetic optimization algorithm, is effective. Finally, the specific performance of the improvement algorithm on practical engineering problems was evaluated. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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29 pages, 12937 KiB

Open AccessArticle

A Variable Step Crow Search Algorithm and Its Application in Function Problems

by Yuqi Fan, Huimin Yang, Yaping Wang, Zunshan Xu and Daoxiang Lu

Biomimetics 2023, 8(5), 395; https://doi.org/10.3390/biomimetics8050395 - 28 Aug 2023

Cited by 4 | Viewed by 1938

Abstract

Optimization algorithms are popular to solve different problems in many fields, and are inspired by natural principles, animal living habits, plant pollinations, chemistry principles, and physic principles. Optimization algorithm performances will directly impact on solving accuracy. The Crow Search Algorithm (CSA) is a [...] Read more.

Optimization algorithms are popular to solve different problems in many fields, and are inspired by natural principles, animal living habits, plant pollinations, chemistry principles, and physic principles. Optimization algorithm performances will directly impact on solving accuracy. The Crow Search Algorithm (CSA) is a simple and efficient algorithm inspired by the natural behaviors of crows. However, the flight length of CSA is a fixed value, which makes the algorithm fall into the local optimum, severely limiting the algorithm solving ability. To solve this problem, this paper proposes a Variable Step Crow Search Algorithm (VSCSA). The proposed algorithm uses the cosine function to enhance CSA searching abilities, which greatly improves both the solution quality of the population and the convergence speed. In the update phase, the VSCSA increases population diversities and enhances the global searching ability of the basic CSA. The experiment used 14 test functions,2017 CEC functions, and engineering application problems to compare VSCSA with different algorithms. The experiment results showed that VSCSA performs better in fitness values, iteration curves, box plots, searching paths, and the Wilcoxon test results, which indicates that VSCSA has strong competitiveness and sufficient superiority. The VSCSA has outstanding performances in various test functions and the searching accuracy has been greatly improved. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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34 pages, 1054 KiB

Open AccessArticle

Fault Diagnosis in Analog Circuits Using Swarm Intelligence

by Nadia Nedjah, Jalber Dinelli Luna Galindo, Luiza de Macedo Mourelle and Fernanda Duarte Vilela Reis de Oliveira

Biomimetics 2023, 8(5), 388; https://doi.org/10.3390/biomimetics8050388 - 25 Aug 2023

Cited by 1 | Viewed by 2466

Abstract

Open or short-circuit faults, as well as discrete parameter faults, are the most commonly used models in the simulation prior to testing methodology. However, since analog circuits exhibit continuous responses to input signals, faults in specific circuit elements may not fully capture all [...] Read more.

Open or short-circuit faults, as well as discrete parameter faults, are the most commonly used models in the simulation prior to testing methodology. However, since analog circuits exhibit continuous responses to input signals, faults in specific circuit elements may not fully capture all potential component faults. Consequently, diagnosing faults in analog circuits requires three key aspects: identifying faulty components, determining faulty element values, and considering circuit tolerance constraints. To tackle this problem, a methodology is proposed and implemented for fault diagnosis using swarm intelligence. The investigated optimization techniques are Particle Swarm Optimization (PSO) and the Bat Algorithm (BA). In this methodology, the nonlinear equations of the tested circuit are employed to calculate its parameters. The primary objective is to identify the specific circuit component that could potentially exhibit the fault by comparing the responses obtained from the actual circuit and the responses obtained through the optimization process. Two circuits are used as case studies to evaluate the performance of the proposed methodologies: the Tow–Thomas Biquad filter (case study 1) and the Butterworth filter (case study 2). The proposed methodologies are able to identify or at least reduce the number of possible faulty components. Four main performance metrics are extracted: accuracy, precision, sensitivity, and specificity. The BA technique demonstrates superior performance by utilizing the maximum combination of accessible nodes in the tested circuit, with an average accuracy of 95.5%, while PSO achieved only 93.9%. Additionally, the BA technique outperforms in terms of execution time, with an average time reduction of 7.95% reduction for the faultless circuit and an 8.12% reduction for the faulty cases. Compared to the machine-learning-based approach, using BA with the proposed methodology achieves similar accuracy rates but does not require any datasets nor any time-demanding training to proceed with circuit diagnostic. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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44 pages, 9853 KiB

Open AccessArticle

PSO-Incorporated Hybrid Artificial Hummingbird Algorithm with Elite Opposition-Based Learning and Cauchy Mutation: A Case Study of Shape Optimization for CSGC–Ball Curves

by Kang Chen, Liuxin Chen and Gang Hu

Biomimetics 2023, 8(4), 377; https://doi.org/10.3390/biomimetics8040377 - 18 Aug 2023

Cited by 9 | Viewed by 2333

Abstract

With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to [...] Read more.

With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize complex composite shape-adjustable generalized cubic Ball (CSGC–Ball, for short) curves. Firstly, the Artificial Hummingbird algorithm (AHA), as a newly proposed meta-heuristic algorithm, has the advantages of simple structure and easy implementation and can quickly find the global optimal solution. However, there are still limitations, such as low convergence accuracy and the tendency to fall into local optimization. Therefore, this paper proposes the HAHA based on the original AHA, combined with the elite opposition-based learning strategy, PSO, and Cauchy mutation, to increase the population diversity of the original algorithm, avoid falling into local optimization, and thus improve the accuracy and rate of convergence of the original AHA. Twenty-five benchmark test functions and the CEC 2022 test suite are used to evaluate the overall performance of HAHA, and the experimental results are statistically analyzed using Friedman and Wilkerson rank sum tests. The experimental results show that, compared with other advanced algorithms, HAHA has good competitiveness and practicality. Secondly, in order to better realize the modeling of complex curves in engineering, the CSGC–Ball curves with global and local shape parameters are constructed based on SGC–Ball basis functions. By changing the shape parameters, the whole or local shape of the curves can be adjusted more flexibly. Finally, in order to make the constructed curve have a more ideal shape, the CSGC–Ball curve-shape optimization model is established based on the minimum curve energy value, and the proposed HAHA is used to solve the established shape optimization model. Two representative numerical examples comprehensively verify the effectiveness and superiority of HAHA in solving CSGC–Ball curve-shape optimization problems. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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25 pages, 6665 KiB

Open AccessArticle

A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems

by Sen Yang, Linbo Zhang, Xuesen Yang, Jiayun Sun and Wenhao Dong

Biomimetics 2023, 8(4), 348; https://doi.org/10.3390/biomimetics8040348 - 6 Aug 2023

Cited by 1 | Viewed by 1818

Abstract

The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a [...] Read more.

The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a double-opposite learning mechanism, an adaptive spiral search strategy, an offset distribution estimation strategy, and a modified cosine acceleration function formula into the original AOA, aiming to improve the local exploitation and global exploration capability of the original AOA. In the proposed ASFAOA, a dual-opposite learning strategy is utilized to enhance population diversity by searching the problem space a lot better. The spiral search strategy of the tuna swarm optimization is introduced into the addition and subtraction strategy of AOA to enhance the AOA’s ability to jump out of the local optimum. An offset distribution estimation strategy is employed to effectively utilize the dominant population information for guiding the correct individual evolution. In addition, an adaptive cosine acceleration function is proposed to perform a better balance between the exploitation and exploration capabilities of the AOA. To demonstrate the superiority of the proposed ASFAOA, two experiments are conducted using existing state-of-the-art algorithms. First, The CEC 2017 benchmark function was applied with the aim of evaluating the performance of ASFAOA on the test function through mean analysis, convergence analysis, stability analysis, Wilcoxon signed rank test, and Friedman’s test. The proposed ASFAOA is then utilized to solve the wireless sensor coverage problem and its performance is illustrated by two sets of coverage problems with different dimensions. The results and discussion show that ASFAOA outperforms the original AOA and other comparison algorithms. Therefore, ASFAOA is considered as a useful technique for practical optimization problems. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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23 pages, 3088 KiB

Open AccessArticle

A Subtraction-Average-Based Optimizer for Solving Engineering Problems with Applications on TCSC Allocation in Power Systems

by Ghareeb Moustafa, Mohamed A. Tolba, Ali M. El-Rifaie, Ahmed Ginidi, Abdullah M. Shaheen and Slim Abid

Biomimetics 2023, 8(4), 332; https://doi.org/10.3390/biomimetics8040332 - 27 Jul 2023

Cited by 17 | Viewed by 2203

Abstract

The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching [...] Read more.

The present study introduces a subtraction-average-based optimization algorithm (SAOA), a unique enhanced evolutionary technique for solving engineering optimization problems. The typical SAOA works by subtracting the average of searcher agents from the position of population members in the search space. To increase searching capabilities, this study proposes an improved SAO (ISAO) that incorporates a cooperative learning technique based on the leader solution. First, after considering testing on different standard mathematical benchmark functions, the proposed ISAOA is assessed in comparison to the standard SAOA. The simulation results declare that the proposed ISAOA establishes great superiority over the standard SAOA. Additionally, the proposed ISAOA is adopted to handle power system applications for Thyristor Controlled Series Capacitor (TCSC) allocation-based losses reduction in electrical power grids. The SAOA and the proposed ISAOA are employed to optimally size the TCSCs and simultaneously select their installed transmission lines. Both are compared to two recent algorithms, the Artificial Ecosystem Optimizer (AEO) and AQuila Algorithm (AQA), and two other effective and well-known algorithms, the Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO). In three separate case studies, the standard IEEE-30 bus system is used for this purpose while considering varying numbers of TCSC devices that will be deployed. The suggested ISAOA’s simulated implementations claim significant power loss reductions for the three analyzed situations compared to the GWO, AEO, PSO, and AQA. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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24 pages, 975 KiB

Open AccessArticle

A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting

by Faten Khalid Karim, Doaa Sami Khafaga, Marwa M. Eid, S. K. Towfek and Hend K. Alkahtani

Biomimetics 2023, 8(3), 321; https://doi.org/10.3390/biomimetics8030321 - 20 Jul 2023

Cited by 25 | Viewed by 3720

Abstract

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data [...] Read more.

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson’s correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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17 pages, 17261 KiB

Open AccessArticle

Biomimetics Design of Tooth Root Zone at Cylindrical Gears Profile

by Ivana Atanasovska, Dejan Momcilovic, Tatjana Lazovic, Aleksandar Marinkovic and Natasa Soldat

Biomimetics 2023, 8(3), 308; https://doi.org/10.3390/biomimetics8030308 - 12 Jul 2023

Viewed by 2650

Abstract

During the last few decades, the requirements for modern machine elements in terms of size reduction, increasing the energy efficiency, and a higher load capacity of standard and non-standard gears have been very prevalent issues. Within these demands, the main goals are the [...] Read more.

During the last few decades, the requirements for modern machine elements in terms of size reduction, increasing the energy efficiency, and a higher load capacity of standard and non-standard gears have been very prevalent issues. Within these demands, the main goals are the optimization of the gears’ tooth profiles, as well as the investigation of new tooth profile designs. The presented design idea is based on the optimal solutions inspired by nature. Special attention is paid to the new design of the tooth root zones of spur gears in order to decrease the stress concentration values and increase the tooth root fatigue resistance. The finite element method is used for stress and strain state calculations, and the particular gear pair is modeled and optimized for these purposes. For tooth root strength analysis, the estimations are based on the theory of critical distances and the stress gradients obtained through finite element analysis. The obtained stress gradients have shown important improvements in the stress distribution in the transition zone optimized by biomimetics. An analysis of the material variation influence is also performed. Based on the investigations of a particular gear pair, a significant stress reduction of about 7% for steel gears and about 10.3% for cast iron gears is obtained for tooth roots optimized by bio-inspired design. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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23 pages, 4049 KiB

Open AccessArticle

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

by Yinggao Yue, Li Cao, Haishao Chen, Yaodan Chen and Zhonggen Su

Biomimetics 2023, 8(3), 306; https://doi.org/10.3390/biomimetics8030306 - 12 Jul 2023

Cited by 14 | Viewed by 1818

Abstract

The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result [...] Read more.

The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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33 pages, 23533 KiB

Open AccessArticle

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

by Liguo Yao, Guanghui Li, Panliang Yuan, Jun Yang, Dongbin Tian and Taihua Zhang

Biomimetics 2023, 8(3), 305; https://doi.org/10.3390/biomimetics8030305 - 11 Jul 2023

Cited by 5 | Viewed by 2221

Abstract

The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is [...] Read more.

The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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17 pages, 433 KiB

Open AccessArticle

Binary Restructuring Particle Swarm Optimization and Its Application

by Jian Zhu, Jianhua Liu, Yuxiang Chen, Xingsi Xue and Shuihua Sun

Biomimetics 2023, 8(2), 266; https://doi.org/10.3390/biomimetics8020266 - 17 Jun 2023

Cited by 13 | Viewed by 2797

Abstract

Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for [...] Read more.

Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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23 pages, 895 KiB

Open AccessArticle

Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications

by Amal H. Alharbi, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, S. K. Towfek, Nima Khodadadi, Laith Abualigah, Doaa Sami Khafaga and Ayman EM Ahmed

Biomimetics 2023, 8(2), 241; https://doi.org/10.3390/biomimetics8020241 - 7 Jun 2023

Cited by 21 | Viewed by 2037

Abstract

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, [...] Read more.

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm’s regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon’s rank-sum and ANOVA tests. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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16 pages, 8934 KiB

Open AccessArticle

Augmented Harris Hawks Optimizer with Gradient-Based-Like Optimization: Inverse Design of All-Dielectric Meta-Gratings

by Kofi Edee

Biomimetics 2023, 8(2), 179; https://doi.org/10.3390/biomimetics8020179 - 24 Apr 2023

Cited by 3 | Viewed by 2169

Abstract

In this paper, we introduce a new hybrid optimization method for the inverse design of metasurfaces, which combines the original Harris hawks optimizer (HHO) with a gradient-based optimization method. The HHO is a population-based algorithm that mimics the hunting process of hawks tracking [...] Read more.

In this paper, we introduce a new hybrid optimization method for the inverse design of metasurfaces, which combines the original Harris hawks optimizer (HHO) with a gradient-based optimization method. The HHO is a population-based algorithm that mimics the hunting process of hawks tracking prey. The hunting strategy is divided into two phases: exploration and exploitation. However, the original HHO algorithm performs poorly in the exploitation phase and may get trapped and stagnate in a basin of local optima. To improve the algorithm, we propose pre-selecting better initial candidates obtained from a gradient-based-like (GBL) optimization method. The main drawback of the GBL optimization method is its strong dependence on initial conditions. However, like any gradient-based method, GBL has the advantage of broadly and efficiently spanning the design space at the cost of computation time. By leveraging the strengths of both methods, namely GBL optimization and HHO, we show that the proposed hybrid approach, denoted as GBL–HHO, is an optimal scenario for efficiently targeting a class of unseen good global optimal solutions. We apply the proposed method to design all-dielectric meta-gratings that deflect incident waves into a given transmission angle. The numerical results demonstrate that our scenario outperforms the original HHO. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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23 pages, 2854 KiB

Open AccessArticle

Progressive Learning Hill Climbing Algorithm with Energy-Map-Based Initialization for Image Reconstruction

by Yuhui Zhang, Wenhong Wei and Zijia Wang

Biomimetics 2023, 8(2), 174; https://doi.org/10.3390/biomimetics8020174 - 22 Apr 2023

Viewed by 2533

Abstract

Image reconstruction is an interesting yet challenging optimization problem that has several potential applications. The task is to reconstruct an image using a fixed number of transparent polygons. Traditional gradient-based algorithms cannot be applied to the problem since the optimization objective has no [...] Read more.

Image reconstruction is an interesting yet challenging optimization problem that has several potential applications. The task is to reconstruct an image using a fixed number of transparent polygons. Traditional gradient-based algorithms cannot be applied to the problem since the optimization objective has no explicit expression and cannot be represented by computational graphs. Metaheuristic search algorithms are powerful optimization techniques for solving complex optimization problems, especially in the context of incomplete information or limited computational capability. In this paper, we developed a novel metaheuristic search algorithm named progressive learning hill climbing (ProHC) for image reconstruction. Instead of placing all the polygons on a blank canvas at once, ProHC starts from one polygon and gradually adds new polygons to the canvas until reaching the number limit. Furthermore, an energy-map-based initialization operator was designed to facilitate the generation of new solutions. To assess the performance of the proposed algorithm, we constructed a benchmark problem set containing four different types of images. The experimental results demonstrated that ProHC was able to produce visually pleasing reconstructions of the benchmark images. Moreover, the time consumed by ProHC was much shorter than that of the existing approach. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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Review

Jump to: Research

29 pages, 2756 KiB

Open AccessReview

Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions

by Abdullah Alabdulatif and Navod Neranjan Thilakarathne

Biomimetics 2023, 8(4), 373; https://doi.org/10.3390/biomimetics8040373 - 17 Aug 2023

Cited by 18 | Viewed by 5323

Abstract

There is no doubt that the involvement of the Internet of Things (IoT) in our daily lives has changed the way we live and interact as a global community, as IoT enables intercommunication of digital objects around us, creating a pervasive environment. As [...] Read more.

There is no doubt that the involvement of the Internet of Things (IoT) in our daily lives has changed the way we live and interact as a global community, as IoT enables intercommunication of digital objects around us, creating a pervasive environment. As of now, this IoT is found in almost every domain that is vital for human survival, such as agriculture, medical care, transportation, the military, and so on. Day by day, various IoT solutions are introduced to the market by manufacturers towards making our life easier and more comfortable. On the other hand, even though IoT now holds a key place in our lives, the IoT ecosystem has various limitations in efficiency, scalability, and adaptability. As such, biomimicry, which involves imitating the systems found in nature within human-made systems, appeared to be a potential remedy to overcome such challenges pertaining to IoT, which can also be referred to as bio-inspired IoT. In the simplest terms, bio-inspired IoT combines nature-inspired principles and IoT to create more efficient and adaptive IoT solutions, that can overcome most of the inherent challenges pertaining to traditional IoT. It is based on the idea that nature has already solved many challenging problems and that, by studying and mimicking biological systems, we might develop better IoT systems. As of now, this concept of bio-inspired IoT is applied to various fields such as medical care, transportation, cyber-security, agriculture, and so on. However, it is noted that only a few studies have been carried out on this new concept, explaining how these bio-inspired concepts are integrated with IoT. Thus, to fill in the gap, in this study, we provide a brief review of bio-inspired IoT, highlighting how it came into play, its ecosystem, its latest status, benefits, challenges, and future directions. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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70 pages, 8413 KiB

Open AccessReview

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

by Zoran Jakšić, Swagata Devi, Olga Jakšić and Koushik Guha

Biomimetics 2023, 8(3), 278; https://doi.org/10.3390/biomimetics8030278 - 28 Jun 2023

Cited by 40 | Viewed by 13875

Abstract

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being [...] Read more.

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)

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