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A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 9672

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Prof. Dr. Heming Jia

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School of Information Engineering, Sanming University, Sanming 365000, China
Interests: optimization; remora optimization algorithm (ROA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems
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Prof. Dr. Gang Hu

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Department of Applied Mathematics, Xi’an University of Technology, Xi’an, China
Interests: metaheuristic algorithms; computing intelligence; artificial intelligence; complex optimization systems; CAD/CAM; image processing and analysis; path planning; multilevel image segmentation; feature selection; Genghis Khan Shark Optimizer
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Special Issue Information

Dear Colleagues,

As industrialization continues to progress at an unprecedented pace, engineering applications are proliferating, accompanied by a myriad of intricate and diverse challenges. To navigate through these complex real-world problems, a plethora of optimization algorithms have been devised, with swarm intelligence optimization algorithms (SIOAs) occupying a prominent position. SIOAs, drawing inspiration from the collective behaviors exhibited by swarms of insects, animals, or other organisms, have demonstrated remarkable abilities in solving non-convex, nonlinearly constrained, and high-dimensional optimization tasks. Their inherent capability to swiftly converge towards optimal solutions while effectively escaping local optima has been well documented in numerous studies.

The Special Issue "Advances in Swarm Intelligence Optimization Algorithms and Applications" aims to consolidate and showcase the latest breakthroughs and achievements in this burgeoning field. It serves as a platform for interdisciplinary research, fostering collaboration among scholars from diverse backgrounds who are exploring the potential of SIOAs for engineering applications. We invite researchers to submit their original contributions that delve into the theoretical foundations, algorithmic innovations, and practical applications of SIOAs, with a focus on addressing specific challenges and advancing the state-of-the-art.

The scope of this Special Issue encompasses, but is not limited to, the following topics:

Novel SIOAs: The development of new swarm intelligence optimization algorithms, including those inspired by unique swarm behaviors or innovative mechanisms for enhancing exploration, exploitation, and convergence.

Hybridization and Integration: Studies exploring the integration of SIOAs with other optimization techniques, machine learning algorithms, or heuristic methods to create hybrid optimization frameworks that leverage the strengths of each approach.

Theoretical Analysis: In-depth analyses of the mathematical properties, convergence behavior, and complexity of SIOAs, providing insights into their performance and limitations.

Parameter Tuning and Adaptation: Research on adaptive parameter control strategies for SIOAs, aimed at enhancing their robustness, versatility, and performance across different problem domains.

High-Dimensional and Complex Problems: Applications of SIOAs to tackle high-dimensional, multimodal, dynamic, and noisy optimization problems, demonstrating their effectiveness in real-world contexts.

Benchmarking and Comparative Studies: Comparative evaluations of SIOAs using standard and novel benchmark functions, highlighting their strengths and weaknesses relative to other optimization techniques.

Engineering Applications: Case studies showcasing the successful application of SIOAs in solving engineering problems, such as design optimization, production scheduling, network routing, and control systems.

By publishing high-quality research on SIOAs and their applications, this Special Issue aims to promote the dissemination of knowledge, facilitate interdisciplinary collaborations, and inspire further advancements in this exciting field. We encourage researchers to submit their original work, addressing both theoretical and applied aspects of SIOAs, to contribute to this important endeavor.

Prof. Dr. Heming Jia
Prof. Dr. Gang Hu
Guest Editors

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Keywords

swarm intelligence optimization algorithms
particle swarm optimization algorithm
optimization algorithms
meta-heuristics
swarm intelligence
engineering applications
engineering design problems
real-world applications
constraint handling
benchmarks
novel approaches
complicated optimization problems

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Related Special Issue

Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition in Biomimetics (1 article)

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Research

38 pages, 9376 KiB

Open AccessArticle

IA-DTPSO: A Multi-Strategy Integrated Particle Swarm Optimization for Predicting the Total Urban Water Resources in China

by Zheyu Zhu, Jiawei Wang and Kanhua Yu

Biomimetics 2025, 10(4), 233; https://doi.org/10.3390/biomimetics10040233 - 8 Apr 2025

Viewed by 232

Abstract

In order to overcome the drawbacks of low search efficiency and susceptibility to local optimal traps in PSO, this study proposes a multi-strategy particle swarm optimization (PSO) with information acquisition, referred to as IA-DTPSO. Firstly, Sobol sequence initialization on particles to achieve a more uniform initial population distribution is performed. Secondly, an update scheme based on information acquisition is established, which adopts different information processing methods according to the evaluation status of particles at different stages to improve the accuracy of information shared between particles. Then, the Spearman’s correlation coefficient (SCC) is introduced to determine the dimensions that require reverse solution position updates, and the tangent flight strategy is used to improve the inherent single update method of PSO. Finally, a dimension learning strategy is introduced to strengthen individual particles’ activity, thereby ameliorating the entire particle population’s diversity. In order to conduct a comprehensive analysis of IA-DTPSO, its excellent exploration and exploitation (ENE) capability is firstly validated on CEC2022. Subsequently, the performance of IA-DTPSO and other algorithms on different dimensions of CEC2022 is validated, and the results show that IA-DTPSO wins 58.33% and 41.67% of the functions on 10 and 20 dimensions of CEC2022, respectively. Finally, IA-DTPSO is employed to optimize parameters of the time-dependent gray model (1,1,r,ξ,Csz) (TDGM (1,1,r,ξ,Csz)) and applied to simulate and predict total urban water resources (TUWRs) in China. By using four error evaluation indicators, this method is compared with other algorithms and existing models. The results show that the total MAPE (%) value obtained by simulation after IA-DTPSO optimization is 5.9439, which has the smallest error among all comparison methods and models, verifying the effectiveness of this method for predicting TUWRs in China. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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30 pages, 3427 KiB

Open AccessArticle

An Enhanced Team-Oriented Swarm Optimization Algorithm (ETOSO) for Robust and Efficient High-Dimensional Search

by Adel BenAbdennour

Biomimetics 2025, 10(4), 222; https://doi.org/10.3390/biomimetics10040222 - 3 Apr 2025

Viewed by 279

Abstract

This paper introduces the Enhanced Team-Oriented Swarm Optimization (ETOSO) algorithm, a novel refinement of the Team-Oriented Swarm Optimization (TOSO) algorithm aimed at addressing the stagnation problem commonly encountered in nature-inspired optimization approaches. ETOSO enhances TOSO by integrating innovative strategies for exploration and exploitation, resulting in a simplified algorithm that demonstrates superior performance across a broad spectrum of benchmark functions, particularly in high-dimensional search spaces. A comprehensive comparative evaluation and statistical tests against 26 established nature-inspired optimization algorithms (NIOAs) across 15 benchmark functions and dimensions (D = 2, 5, 10, 30, 50, 100, 200) confirm ETOSO’s superiority relative to solution accuracy, convergence speed, computational complexity, and consistency. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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22 pages, 1180 KiB

Open AccessArticle

FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning

by Xuhua Zhao, Yongming Zheng, Jiaxiang Wan, Yehong Li, Donglin Zhu, Zhenyu Xu and Huijuan Lu

Biomimetics 2025, 10(3), 185; https://doi.org/10.3390/biomimetics10030185 - 17 Mar 2025

Viewed by 379

Abstract

Federated learning (FL) is a distributed learning technique that ensures data privacy and has shown significant potential in cross-institutional image analysis. However, existing methods struggle with the inherent dynamic heterogeneity of real-world data, such as changes in cellular differentiation during disease progression or feature distribution shifts due to different imaging devices. This dynamic heterogeneity can cause catastrophic forgetting, leading to reduced performance in medical predictions across stages. Unlike previous federated learning studies that paid insufficient attention to dynamic heterogeneity, this paper proposes the FedDyH framework to address this challenge. Inspired by the adaptive regulation mechanisms of biological systems, this framework incorporates several core modules to tackle the issues arising from dynamic heterogeneity. First, the framework simulates intercellular information transfer through cross-client knowledge distillation, preserving local features while mitigating knowledge forgetting. Additionally, a dynamic regularization term is designed in which the strength can be adaptively adjusted based on real-world conditions. This mechanism resembles the role of regulatory T cells in the immune system, balancing global model convergence with local specificity adjustments to enhance the robustness of the global model while preventing interference from diverse client features. Finally, the framework introduces a genetic algorithm (GA) to simulate biological evolution, leveraging mechanisms such as gene selection, crossover, and mutation to optimize hyperparameter configurations. This enables the model to adaptively find the optimal hyperparameters in an ever-changing environment, thereby improving both adaptability and performance. Prior to this work, few studies have explored the use of optimization algorithms for hyperparameter tuning in federated learning. Experimental results demonstrate that the FedDyH framework improves accuracy compared to the SOTA baseline FedDecorr by 2.59%, 0.55%, and 5.79% on the MNIST, Fashion-MNIST, and CIFAR-10 benchmark datasets, respectively. This framework effectively addresses data heterogeneity issues in dynamic heterogeneous environments, providing an innovative solution for achieving more stable and accurate distributed federated learning. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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28 pages, 14926 KiB

Open AccessArticle

Research on Ship Replenishment Path Planning Based on the Modified Whale Optimization Algorithm

by Qinghua Chen, Gang Yao, Lin Yang, Tangying Liu, Jin Sun and Shuxiang Cai

Biomimetics 2025, 10(3), 179; https://doi.org/10.3390/biomimetics10030179 - 13 Mar 2025

Viewed by 422

Abstract

Ship replenishment path planning has always been a critical concern for researchers in the field of security. This study proposes a modified whale optimization algorithm (MWOA) to address single-task ship replenishment path planning problems. To ensure high-quality initial solutions and maintain population diversity, a hybrid approach combining the nearest neighbor search with random search is employed for initial population generation. Additionally, crossover operations and destroy and repair operators are integrated to update the whale’s position, significantly enhancing the algorithm’s search efficiency and optimization performance. Furthermore, variable neighborhood search is utilized for local optimization to refine the solutions. The proposed MWOA has been tested against several algorithms, including the original whale optimization algorithm, genetic algorithm, ant colony optimization, hybrid particle swarm optimization, and simulated annealing, using traveling salesman problems as benchmarks. Results demonstrate that MWOA outperforms these algorithms in both solution quality and stability. Moreover, when applied to ship replenishment path planning problems of varying scales, MWOA consistently achieves superior performance compared to the other algorithms. The proposed algorithm demonstrates high adaptability in addressing diverse ship replenishment path planning problems, delivering efficient, high-quality, and reliable solutions. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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35 pages, 9062 KiB

Open AccessArticle

A Multi-Strategy Parrot Optimization Algorithm and Its Application

by Yang Yang, Maosheng Fu, Xiancun Zhou, Chaochuan Jia and Peng Wei

Biomimetics 2025, 10(3), 153; https://doi.org/10.3390/biomimetics10030153 - 2 Mar 2025

Viewed by 553

Abstract

Intelligent optimization algorithms are crucial for solving complex engineering problems. The Parrot Optimization (PO) algorithm shows potential but has issues like local-optimum trapping and slow convergence. This study presents the Chaotic–Gaussian–Barycenter Parrot Optimization (CGBPO), a modified PO algorithm. CGBPO addresses these problems in three ways: using chaotic logistic mapping for random initialization to boost population diversity, applying Gaussian mutation to updated individual positions to avoid premature local-optimum convergence, and integrating a barycenter opposition-based learning strategy during iterations to expand the search space. Evaluated on the CEC2017 and CEC2022 benchmark suites against seven other algorithms, CGBPO outperforms them in convergence speed, solution accuracy, and stability. When applied to two practical engineering problems, CGBPO demonstrates superior adaptability and robustness. In an indoor visible light positioning simulation, CGBPO’s estimated positions are closer to the actual ones compared to PO, with the best coverage and smallest average error. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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26 pages, 4719 KiB

Open AccessArticle

An Efficient Multi-Objective White Shark Algorithm

by Wenyan Guo, Yufan Qiang, Fang Dai, Junfeng Wang and Shenglong Li

Biomimetics 2025, 10(2), 112; https://doi.org/10.3390/biomimetics10020112 - 13 Feb 2025

Cited by 1 | Viewed by 585

Abstract

To balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a multi-objective White Shark Optimization algorithm (MONSWSO) tailored for multi-objective optimization. MONSWSO integrates non-dominated sorting and crowding distance into the White Shark Optimization framework to select the optimal solution within the population. The uniformity of the initial population is enhanced through a chaotic reverse initialization learning strategy. The adaptive updating of individual positions is facilitated by an elite-guided forgetting mechanism, which incorporates escape energy and eddy aggregation behavior inspired by marine organisms to improve exploration in key areas. To evaluate the effectiveness of MONSWSO, it is benchmarked against five state-of-the-art multi-objective algorithms using four metrics: inverse generation distance, spatial homogeneity, spatial distribution, and hypervolume on 27 typical problems, including 23 multi-objective functions and 4 multi-objective project examples. Furthermore, the practical application of MONSWSO is demonstrated through an example of optimizing the design of subway tunnel foundation pits. The comprehensive results reveal that MONSWSO outperforms the comparison algorithms, achieving impressive and satisfactory outcomes. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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32 pages, 4886 KiB

Open AccessArticle

Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference

by Weiping Meng, Yang He and Yongquan Zhou

Biomimetics 2025, 10(1), 57; https://doi.org/10.3390/biomimetics10010057 - 15 Jan 2025

Cited by 1 | Viewed by 902

Abstract

This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from falling into a local optimum, the Gaussian mutation mechanism with dynamic variance was introduced, and the migration mutation mechanism was also used to enhance the population diversity of the algorithm. Eighteen benchmark functions were used to compare the proposed method with five classical metaheuristic algorithms and three BOA variable optimization methods. The QLBOA was used to solve the green vehicle routing problem with time windows considering customer preferences. The influence of decision makers’ subjective preferences and weight factors on fuel consumption, carbon emissions, penalty cost, and total cost are analyzed. Compared with three classical optimization algorithms, the experimental results show that the proposed QLBOA has a generally superior performance. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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20 pages, 7258 KiB

Open AccessArticle

MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification

by Guangyu Mu, Jiaxue Li, Zhanhui Liu, Jiaxiu Dai, Jiayi Qu and Xiurong Li

Biomimetics 2025, 10(1), 41; https://doi.org/10.3390/biomimetics10010041 - 10 Jan 2025

Cited by 2 | Viewed by 963

Abstract

With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method’s principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder–Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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25 pages, 4843 KiB

Open AccessArticle

Ameliorated Chameleon Algorithm-Based Shape Optimization of Disk Wang–Ball Curves

by Yan Liang, Rui Yang, Xianzhi Hu and Gang Hu

Biomimetics 2025, 10(1), 3; https://doi.org/10.3390/biomimetics10010003 - 24 Dec 2024

Viewed by 680

Abstract

The shape design and optimization of complex disk curves is a crucial and intractable technique in computer-aided design and manufacturing (CAD/CAM). Based on disk Wang–Ball (DWB) curves, this paper defines a novel combined disk Wang–Ball (CDWB) curve with constrained parameters and investigates the shape optimization of CDWB curves by using the multi-strategy ameliorated chameleon swarm algorithm (MCSA). Firstly, in order to meet the various shape design requirements, the CDWB curves consisting of n DWB curves are defined, and the G¹ and G² geometric continuity conditions for the curves are derived. Secondly, the shape optimization of CDWB curves is considered as a minimization problem with curve energy as the objective, and an optimization model is developed under the constraints of the splicing conditions. Finally, the meta-heuristic algorithm MCSA is introduced to solve the established optimization model to obtain the minimum energy value, and its performance is verified by comparison with other algorithms. The results of representative numerical examples confirm the effectiveness and competitiveness of the MCSA for the CDWB curve shape optimization problems. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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13 pages, 1279 KiB

Open AccessArticle

Predictive Modeling of Hospital Readmission of Schizophrenic Patients in a Spanish Region Combining Particle Swarm Optimization and Machine Learning Algorithms

by Susel Góngora Alonso, Isabel Herrera Montano, Isabel De la Torre Díez, Manuel Franco-Martín, Mohammed Amoon, Jesús-Angel Román-Gallego and María-Luisa Pérez-Delgado

Biomimetics 2024, 9(12), 752; https://doi.org/10.3390/biomimetics9120752 - 11 Dec 2024

Viewed by 899

Abstract

Readmissions are an indicator of hospital care quality; a high readmission rate is associated with adverse outcomes. This leads to an increase in healthcare costs and quality of life for patients. Developing predictive models for hospital readmissions provides opportunities to select treatments and implement preventive measures. The aim of this study is to develop predictive models for the readmission risk of patients with schizophrenia, combining the particle swarm optimization (PSO) algorithm with machine learning classification algorithms. The database used in the study includes a total of 6089 readmission records of patients with schizophrenia. These records were collected from 11 public hospitals in Castilla and León, Spain, in the period 2005–2015. The results of the study show that the Random Forest algorithm combined with PSO achieved the best results across the evaluated performance metrics: AUC = 0.860, recall = 0.959, accuracy = 0.844, and F1-score = 0.907. The development of these new models contributes to -improving patient care. Additionally, they enable preventive measures to reduce costs in healthcare systems. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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42 pages, 13108 KiB

Open AccessArticle

AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems

by Guoping You, Zengtong Lu, Zhipeng Qiu and Hao Cheng

Biomimetics 2024, 9(12), 727; https://doi.org/10.3390/biomimetics9120727 - 28 Nov 2024

Viewed by 1093

Abstract

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO). The adaptive population learning strategy is proposed to improve the global exploration capability of BWO. The introduction of the roulette equilibrium selection strategy allows BWO to have more reference points to choose among during the exploitation phase, which enhances the flexibility of the algorithm. In addition, the adaptive avoidance strategy improves the algorithm’s ability to escape from local optima and enriches the population quality. In order to validate the performance of the proposed AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on the CEC2017 and CEC2022 test sets. Statistical tests, convergence analysis, and stability analysis show that the AMBWO exhibits a superior overall performance. Finally, the applicability and superiority of the AMBWO was further verified by several engineering optimization problems. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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20 pages, 13202 KiB

Open AccessArticle

A Multi-Robot Task Allocation Method Based on the Synergy of the K-Means++ Algorithm and the Particle Swarm Algorithm

by Youdong Yuan, Ping Yang, Hanbing Jiang and Tiange Shi

Biomimetics 2024, 9(11), 694; https://doi.org/10.3390/biomimetics9110694 - 13 Nov 2024

Cited by 2 | Viewed by 1613

Abstract

Addressing challenges in the traditional K-means algorithm, such as the challenge of selecting initial clustering center points and the lack of a maximum limit on the number of clusters, and where the set of tasks in the clusters is not reasonably sorted after the task assignment, which makes the cooperative operation of multiple robots inefficient, this paper puts forward a multi-robot task assignment method based on the synergy of the K-means++ algorithm and the particle swarm optimization (PSO) algorithm. According to the processing capability of the robots, the K-means++ algorithm that limits the maximum number of clusters is used to cluster the target points of the task. The clustering results are assigned to the multi-robot system using the PSO algorithm based on the distances between the robots and the centers of the clusters, which divides the multi-robot task assignment problem into a multiple traveling salesmen problem. Then, the PSO algorithm is used to optimize the ordering of the task sets in each cluster for the multiple traveling salesmen problem. An experimental verification platform is established by building a simulation and physical experiment platform utilizing the Robot Operating System (ROS). The findings indicate that the proposed algorithm outperforms both the clustering-based market auction algorithm and the non-clustering particle swarm algorithm, enhancing the efficiency of collaborative operations among multiple robots. Full article

(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications)

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