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Biomimetics
Special Issues
Beetle Antennae Search (BAS) Algorithm's Variants and Application
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Beetle Antennae Search (BAS) Algorithm's Variants and Application

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

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 9357

Special Issue Editors

Dr. Ameer Hamza Khan

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Guest Editor
Department of Land Surveying and Geo-Informatics, Smart City Research Institute, The Hong Kong Polytechnic University, Hong Kong, China
Interests: SLAM; control systems; robotics; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xinwei Cao

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Guest Editor
School of Business, Jiangnan University, Wuxi 214122, China
Interests: smart finance; intelligent decision making; management and control; fintech; robot decision making
Dr. Bolin Liao

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Guest Editor
College of Computer Science and Engineering, Jishou University, Jishou 416000, China
Interests: controller design; robotics; dynamic systems; control theory
Special Issues, Collections and Topics in MDPI journals
Dr. Vasilios N. Katsikis

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Guest Editor
Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Zografou, Greece
Interests: linear and multilinear algebra; numerical linear algebra; neural networks; intelligent optimization; mathematical finance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Predrag S. Stanimirovic

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Guest Editor
Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18106 Niš, Serbia
Interests: numerical linear algebra; operations research; nonlinear optimization; heuristic optimization; hybrid methods of optimization; gradient neural networks; zeroing neural networks; symbolic computation
Special Issues, Collections and Topics in MDPI journals
Dr. Ivona Brajević

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Guest Editor
Faculty of Applied Management, Economics and Finance, University Business Academy, Jevrejska 24, 11000 Belgrade, Serbia
Interests: artificial intelligence; nature-inspired metaheuristics; constrained optimization

Special Issue Information

Dear Colleagues,

Efficient optimization algorithms are an integral part of any real-world system. Conventional gradient-based optimization techniques put several constraints on the type of objective function that can be solved. For example, an analytical model of the system should be known in advance, and the model should be continuous and differentiable, thus failing on discrete systems. Even discounting the fact that the number of accurate analytical models is  few, real-world optimization problems are usually multimodal (non-convex), where gradient-based methods can only reach locally optimum solutions. Similarly, the computation of gradients and Hessians is a computationally expensive task.

Nature-inspired metaheuristic optimization algorithms present an efficient alternative to these gradient-based algorithms. The fundamental principle of these algorithms is biomimetics, i.e., mimicking the behavior of biological systems to solve an optimization problem. Biological evolution is an optimization process with the goal of maximizing the probability of the survival of a species. Over billions of years of evolution, guided by the principle of natural selection, biological beings have developed behavioral characteristics matching those of an optimization algorithm. For example, the class of evolutionary algorithms is inspired by the process of genetic mutations and the survival of the fittest. Similarly, the PSO algorithm is inspired by the swarming behavior of birds and their ability to accomplish a task in a decentralized manner.

Another important algorithm that has recently gained research attention is called Beetle Antennae Search (BAS), which is inspired by the foraging behavior of beetles. BAS is of particular interest because, unlike other swarm-based algorithms, it only uses single-search particles to search for an optimal solution, making it computationally efficient. One of the primary advantages of BAS is its ability to optimize the performance of a system in a model-free manner (i.e., the analytical formula of the system is not required). Being gradient-free, it can perform equally well on discrete systems.

We are organizing a Special Issue to gather the latest research related to BAS and its application in real-world scenarios. We hope the applications of a bio-inspired metaheuristic algorithm in real-world systems will draw greater research attention to biomimetic.

Dr. Ameer Hamza Khan
Dr. Xinwei Cao
Dr. Bolin Liao
Dr. Vasilios N. Katsikis
Prof. Dr. Predrag S. Stanimirovic
Dr. Ivona Brajević
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bio-inspired algorithms
  • metaheuristic optimization

Published Papers (5 papers)

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Research

16 pages, 3254 KiB  
Article
Maximization of Power Density of Direct Methanol Fuel Cell for Greener Energy Generation Using Beetle Antennae Search Algorithm and Fuzzy Modeling
by Ahmed Al Shouny, Hegazy Rezk, Enas Taha Sayed, Mohammad Ali Abdelkareem, Usama Hamed Issa, Yehia Miky and Abdul Ghani Olabi
Biomimetics 2023, 8(7), 557; https://doi.org/10.3390/biomimetics8070557 - 20 Nov 2023
Viewed by 1185
Abstract
Direct methanol fuel cells (DMFCs) are promising form of energy conversion technology that have the potential to take the role of lithium-ion batteries in portable electronics and electric cars. To increase the efficiency of DMFCs, many operating conditions ought to be optimized. Developing [...] Read more.
Direct methanol fuel cells (DMFCs) are promising form of energy conversion technology that have the potential to take the role of lithium-ion batteries in portable electronics and electric cars. To increase the efficiency of DMFCs, many operating conditions ought to be optimized. Developing a reliable fuzzy model to simulate DMFCs is a major objective. To increase the power output of a DMFC, three process variables are considered: temperature, methanol concentration, and oxygen flow rate. First, a fuzzy model of the DMFC was developed using experimental data. The best operational circumstances to increase power density were then determined using the beetle antennae search (BAS) method. The RMSE values for the fuzzy DMFC model are 0.1982 and 1.5460 for the training and testing data. For training and testing, the coefficient of determination (R2) values were 0.9977 and 0.89, respectively. Thanks to fuzzy logic, the RMSE was reduced by 88% compared to ANOVA. It decreased from 7.29 (using ANOVA) to 0.8628 (using fuzzy). The fuzzy model’s low RMSE and high R2 values show that the modeling phase was successful. In comparison with the measured data and RSM, the combination of fuzzy modeling and the BAS algorithm increased the power density of the DMFC by 8.88% and 7.5%, respectively, and 75 °C, 1.2 M, and 400 mL/min were the ideal values for temperature, methanol concentration, and oxygen flow rate, respectively. Full article
(This article belongs to the Special Issue Beetle Antennae Search (BAS) Algorithm's Variants and Application)
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47 pages, 1881 KiB  
Article
Binarization of Metaheuristics: Is the Transfer Function Really Important?
by José Lemus-Romani, Broderick Crawford, Felipe Cisternas-Caneo, Ricardo Soto and Marcelo Becerra-Rozas
Biomimetics 2023, 8(5), 400; https://doi.org/10.3390/biomimetics8050400 - 01 Sep 2023
Cited by 2 | Viewed by 992
Abstract
In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are [...] Read more.
In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field. Full article
(This article belongs to the Special Issue Beetle Antennae Search (BAS) Algorithm's Variants and Application)
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15 pages, 1397 KiB  
Article
Research on Logistics Distribution Center Location Based on Hybrid Beetle Antennae Search and Rain Algorithm
by Zhimin Mei, Xuexin Chi and Rui Chi
Biomimetics 2022, 7(4), 194; https://doi.org/10.3390/biomimetics7040194 - 07 Nov 2022
Cited by 2 | Viewed by 1399
Abstract
The location of logistics distribution centers is a crucial issue in modern logistics distribution systems. In order to obtain a more reasonable solution, an effective optimization algorithm is essential. This paper proposes a new hybrid method, named the beetle antennae search–rain algorithm (BRA), [...] Read more.
The location of logistics distribution centers is a crucial issue in modern logistics distribution systems. In order to obtain a more reasonable solution, an effective optimization algorithm is essential. This paper proposes a new hybrid method, named the beetle antennae search–rain algorithm (BRA), for the problem of logistics distribution centers’ location. The innovation of the BRA is embodied in three aspects. Firstly, the beetle antennae search (BAS) algorithm is embedded into the rain algorithm (RA); thus, the BAS is improved from an individual search to a swarm intelligent search and the global search ability is improved. Secondly, the search direction strategy of the BAS algorithm is incorporated into the RA, which can improve response speed while ensuring optimization performance. Finally, the search precision is improved by the mechanism of eliminating the inferior solution and generating a new solution. The BRA is tested on 10 benchmark functions and applied to solve the logistics distribution centers’ location problem. The performance of the BRA is compared to that of several classical heuristics by using relevant evaluation indexes and dynamic optimization convergence graphs. Experimental results show that the BRA outperforms the BAS algorithm, the RA and some other classic heuristics. It is also revealed that the BRA is an effective and competitive algorithm for logistics distribution centers’ location. Full article
(This article belongs to the Special Issue Beetle Antennae Search (BAS) Algorithm's Variants and Application)
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20 pages, 27809 KiB  
Article
DGFlow-SLAM: A Novel Dynamic Environment RGB-D SLAM without Prior Semantic Knowledge Based on Grid Segmentation of Scene Flow
by Fei Long, Lei Ding and Jianfeng Li
Biomimetics 2022, 7(4), 163; https://doi.org/10.3390/biomimetics7040163 - 13 Oct 2022
Cited by 6 | Viewed by 1771
Abstract
Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic objects, and their processing speed is inversely proportional [...] Read more.
Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic objects, and their processing speed is inversely proportional to the recognition accuracy. To simultaneously enhance the speed and accuracy for recognizing dynamic objects in different environments, a novel SLAM system without prior semantics called DGFlow-SLAM is proposed in this paper. A novel grid segmentation method is used in the system to segment the scene flow, and then an adaptive threshold method is used to roughly detect the dynamic objects. Based on this, a deep mean clustering segmentation method is applied to find potential dynamic targets. Finally, the results of grid segmentation and depth mean clustering segmentation are jointly used to find moving objects accurately, and all the feature points of the moving objects are removed on the premise of retaining the static part of the moving object. The experimental results show that on the dynamic sequence dataset of TUM RGB-D, compared with the DynaSLAM system with the highest accuracy for detecting moderate and violent motion and the DS-SLAM with the highest accuracy for detecting slight motion, DGflow-SLAM obtains similar accuracy results and improves the accuracy by 7.5%. In addition, DGflow-SLAM is 10 times and 1.27 times faster than DynaSLAM and DS-SLAM, respectively. Full article
(This article belongs to the Special Issue Beetle Antennae Search (BAS) Algorithm's Variants and Application)
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34 pages, 7750 KiB  
Article
Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
by Zuyan Chen, Adam Francis, Shuai Li, Bolin Liao, Dunhui Xiao, Tran Thu Ha, Jianfeng Li, Lei Ding and Xinwei Cao
Biomimetics 2022, 7(4), 144; https://doi.org/10.3390/biomimetics7040144 - 27 Sep 2022
Cited by 33 | Viewed by 3486
Abstract
A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as [...] Read more.
A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92. Full article
(This article belongs to the Special Issue Beetle Antennae Search (BAS) Algorithm's Variants and Application)
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