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Biomimetics
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Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition
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Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 21722

Special Issue Editors

Prof. Dr. Heming Jia

E-Mail Website
Guest Editor
School of Information Engineering, Sanming University, Sanming, China
Interests: Remora Optimization Algorithm (ROA); Crayfish Optimization Algorithm (COA); Catch Fish Optimization Algorithm (CFOA); 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
Special Issues, Collections and Topics in MDPI journals
Dr. Laith Abualigah

E-Mail Website
Guest Editor
Faculty of Information Technology, Al Al-Bayt University, Mafraq, Jordan
Interests: arithmetic optimization algorithm (AOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling; optimization algorithms; evolutionary computations; information retrieval; text clustering; feature selection; combinatorial problems; optimization; advanced machine learning; big data; natural language processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xuewen Xia

E-Mail Website
Guest Editor
College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, China
Interests: particle swarm optimization (PSO); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; feature selection; combinatorial problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development in industrialization, engineering applications are becoming more and more frequent, as are wide and various engineering problems associated with such development. To solve these complex real-world problems, a host of optimization algorithms are proposed, and bio-inspired optimization algorithms account for a large proportion. The literature shows that bio-inspired optimization algorithms with the capability of rapidly converging and escaping from local optimal states could solve complex problems, such as non-convex, nonlinear constraints, and high-dimensional problems. Due to the sufficiently good performance of these optimization algorithms, through an exploration and exploitation process, accurate and adequate results can eventually be produced at a small cost.

The purpose of this Special Issue is to capture the recent contribution of high-quality papers focusing on interdisciplinary research on the optimization algorithm for engineering applications using methods that are inspired by the dynamic and intelligent behavior of creatures, such as hunting, mating, and other social behaviors. We invite researchers to submit their original contributions addressing particular challenging aspects of bio-inspired optimization algorithms from theoretical and applied viewpoints. The topics of this Special Issue include (but are not limited to) the following:

  • Bio-inspired optimization algorithms;
  • Optimization algorithms;
  • Metaheuristics;
  • Swarm intelligence;
  • Engineering applications;
  • Engineering design problems;
  • Real-world applications;
  • Feature selection;
  • Image segmentation;
  • Constraint handling;
  • Benchmarks;
  • Novel approaches;
  • Complicated optimization problems;
  • Industrial problems.

Prof. Dr. Heming Jia
Dr. Laith Abualigah
Prof. Dr. Xuewen Xia
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 optimization algorithms
  • optimization algorithms
  • engineering application
  • metaheuristic algorithms
  • soft computing

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Related Special Issue

Published Papers (21 papers)

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Research

26 pages, 4493 KiB  
Article
Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration
by Jicheng Peng, Qianshuai Wang, Bingyu Jin, Yong Zhang and Kelin Lu
Biomimetics 2024, 9(9), 510; https://doi.org/10.3390/biomimetics9090510 - 23 Aug 2024
Viewed by 450
Abstract
This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system’s observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We [...] Read more.
This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system’s observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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43 pages, 24842 KiB  
Article
MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems
by Zhihao Fu, Zhichun Li, Yongkang Li and Haoyu Chen
Biomimetics 2024, 9(9), 509; https://doi.org/10.3390/biomimetics9090509 - 23 Aug 2024
Viewed by 472
Abstract
Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence [...] Read more.
Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence accuracy, getting rid of local traps, and so on. To further enhance the performance of CFOA, a multi-strategy improved catch fish optimization algorithm (MICFOA) is proposed in this paper. In the exploration phase, we propose a Lévy-based differential independent search strategy to enhance the global search capability of the algorithm while minimizing the impact on the convergence speed. Secondly, in the exploitation phase, a weight-balanced selection mechanism is used to maintain population diversity, enhance the algorithm’s ability to get rid of local optima during the search process, and effectively boost the convergence accuracy. Furthermore, the structure of CFOA is also modified in this paper. A fishermen position replacement strategy is added at the end of the algorithm as a way to strengthen the robustness of the algorithm. To evaluate the performance of MICFOA, a comprehensive comparison with nine other metaheuristic algorithms is performed on the 10/30/50/100 dimensions of the CEC 2017 test functions and the 10/20 dimensions of the CEC2022 test functions. Statistical experiments show that MICFOA has more significant dominance in numerical optimization problems, and its overall performance outperforms the CFOA, PEOA, TLBO, COA, ARO, EDO, YDSE, and other state-of-the-art algorithms such as LSHADE, JADE, IDE-EDA, and APSM-jSO. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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57 pages, 22344 KiB  
Article
Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems
by Yuncheng Dong, Ruichen Tang and Xinyu Cai
Biomimetics 2024, 9(8), 500; https://doi.org/10.3390/biomimetics9080500 - 17 Aug 2024
Viewed by 610
Abstract
In order to further improve performance of the Slime Mould Algorithm, the Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) is proposed in this paper. There are three main modifications to SMA. Firstly, a leader covariance learning strategy is proposed to replace the anisotropic search [...] Read more.
In order to further improve performance of the Slime Mould Algorithm, the Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) is proposed in this paper. There are three main modifications to SMA. Firstly, a leader covariance learning strategy is proposed to replace the anisotropic search operator in SMA to ensure that the agents can evolve in a better direction during the optimization process. Secondly, the best agent is further modified with an improved non-monopoly search mechanism to boost the algorithm’s exploitation and exploration capabilities. Finally, a random differential restart mechanism is developed to assist SMA in escaping from local optimality and increasing population diversity when it is stalled. The impacts of three strategies are discussed, and the performance of EMSMA is evaluated on the CEC2017 suite and CEC2022 test suite. The numerical and statistical results show that EMSMA has excellent performance on both test suites and is superior to the SMA variants such as DTSMA, ISMA, AOSMA, LSMA, ESMA, and MSMA in terms of convergence accuracy, convergence speed, and stability. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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42 pages, 12487 KiB  
Article
Fractional-Order Boosted Hybrid Young’s Double-Slit Experimental Optimizer for Truss Topology Engineering Optimization
by Song Qin, Junling Liu, Xiaobo Bai and Gang Hu
Biomimetics 2024, 9(8), 474; https://doi.org/10.3390/biomimetics9080474 - 5 Aug 2024
Viewed by 590
Abstract
Inspired by classical experiments that uncovered the inherent properties of light waves, Young’s Double-Slit Experiment (YDSE) optimization algorithm represents a physics-driven meta-heuristic method. Its unique search mechanism and scalability have attracted much attention. However, when facing complex or high-dimensional problems, the YDSE optimizer, [...] Read more.
Inspired by classical experiments that uncovered the inherent properties of light waves, Young’s Double-Slit Experiment (YDSE) optimization algorithm represents a physics-driven meta-heuristic method. Its unique search mechanism and scalability have attracted much attention. However, when facing complex or high-dimensional problems, the YDSE optimizer, although striking a good balance between global and local searches, does not converge as fast as it should and is prone to fall into local optimums, thus limiting its application scope. A fractional-order boosted hybrid YDSE, called FYDSE, is proposed in this article. FYDSE employs a multi-strategy mechanism to jointly address the YDSE problems and enhance its ability to solve complex problems. First, a fractional-order strategy is introduced into the dark edge position update of FYDSE to ensure more efficient use of the search potential of a single neighborhood space while reducing the possibility of trapping in a local best. Second, piecewise chaotic mapping is constructed at the initial stage of the population to obtain better-distributed initial solutions and increase the convergence rate to the optimal position. Moreover, the low exploration space is extended by using a dynamic opposition strategy, which improves the probability of acquisition of a globally optimal solution. Finally, by introducing the vertical operator, FYDSE can better balance global exploration and local exploitation and explore new unknown areas. The numerical results show that FYDSE outperforms YDSE in 11 (91.6%) of cec2022 sets. In addition, FYDSE performs best in 8 (66.6%) among all algorithms. Compared with the 11 methods, FYDSE obtains the optimal best and average weights for the 20-bar, 24-bar, and 72-bar truss problems, which proves its efficient optimization capability for difficult optimization cases. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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29 pages, 573 KiB  
Article
A Survey on Biomimetic and Intelligent Algorithms with Applications
by Hao Li, Bolin Liao, Jianfeng Li and Shuai Li
Biomimetics 2024, 9(8), 453; https://doi.org/10.3390/biomimetics9080453 - 24 Jul 2024
Viewed by 521
Abstract
The question “How does it work” has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in [...] Read more.
The question “How does it work” has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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19 pages, 6865 KiB  
Article
Investigating the Influence of Counterflow Regions on the Hydrodynamic Performance of Biomimetic Robotic Fish
by Yanling Gong, Ming Wang, Qianchuan Zhao, Ruilong Wang, Lingchen Zuo, Xuehan Zheng and He Gao
Biomimetics 2024, 9(8), 452; https://doi.org/10.3390/biomimetics9080452 - 24 Jul 2024
Viewed by 527
Abstract
Biomimetic robotic fish are a novel approach to studying quiet, highly agile, and efficient underwater propulsion systems, attracting significant interest from experts in robotics and engineering. These versatile robots showcase their ability to operate effectively in various water conditions. Nevertheless, the comprehension of [...] Read more.
Biomimetic robotic fish are a novel approach to studying quiet, highly agile, and efficient underwater propulsion systems, attracting significant interest from experts in robotics and engineering. These versatile robots showcase their ability to operate effectively in various water conditions. Nevertheless, the comprehension of the swimming mechanics and the evolution of the flow field of flexible robots in counterflow regions is still unknown. This paper presents a framework for the self-propulsion of robotic fish that imitates biological characteristics. The method utilizes computational fluid dynamics to analyze the hydrodynamic efficiency of the organisms at different frequencies of tail movement, under both still and opposing flow circumstances. Moreover, this study clarifies the mechanisms that explain how changes in the aquatic environment affect the speed and efficiency of propulsion. It also examines the most effective swimming tactics for places with counterflow. The results suggest that the propulsion effectiveness of robotic fish in counterflow locations does not consistently correspond to various tail-beat frequencies. By utilizing vorticity maps, a comparative analysis can identify situations when counterflow zones improve the efficiency of propulsion. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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24 pages, 8104 KiB  
Article
Optimal Design of Formulas for a Single Degree of Freedom Tuned Mass Damper Parameter Using a Genetic Algorithm and H2 Norm
by Seunggoo Kim, Donwoo Lee and Seungjae Lee
Biomimetics 2024, 9(8), 450; https://doi.org/10.3390/biomimetics9080450 - 24 Jul 2024
Viewed by 567
Abstract
One of the researchers’ concerns in structural engineering is to control the dynamic behavior of structures efficiently. The TMD (tuned mass damper) is one of the effective methods of controlling the vibration of structures, and various numerical techniques have been proposed to find [...] Read more.
One of the researchers’ concerns in structural engineering is to control the dynamic behavior of structures efficiently. The TMD (tuned mass damper) is one of the effective methods of controlling the vibration of structures, and various numerical techniques have been proposed to find the optimal parameters of the TMD. This paper develops a new explicit formula to derive the optimal parameters of the TMD of a single degree of freedom (SDOF) structure under seismic load using a genetic algorithm (GA). In addition, the state-space model and the H2 norm function are used to identify the optimal frequency ratio and damping ratio of the TMD that minimize the overall vibration energy of the structure. The MATLAB curve fitting toolbox is used for the explicit formula proposal, and the validity of the proposed formula is verified through multidimensional performance verification technique. Finally, the TMD parameters of the SDOF structure are applied to the multi-degrees of freedom (MDOF) structure to compare and analyze with the existing research results, and the results of the explicit formula proposed in this paper are confirmed to be excellent. This paper can suggest a new direction for determining the optimal TMD parameters using a GA and can be effectively applied to vibration control problems of various structures. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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27 pages, 7202 KiB  
Article
The Basics of Evolution Strategies: The Implementation of the Biomimetic Optimization Method in Educational Modules
by Olga Speck, Thomas Speck, Sabine Baur and Michael Herdy
Biomimetics 2024, 9(7), 439; https://doi.org/10.3390/biomimetics9070439 - 18 Jul 2024
Viewed by 700
Abstract
With a focus on education and teaching, we provide general background information on bioinspired optimization methods by comparing the concept of optimization and the search for an optimum in engineering and biology. We introduce both the principles of Darwinian evolution and the basic [...] Read more.
With a focus on education and teaching, we provide general background information on bioinspired optimization methods by comparing the concept of optimization and the search for an optimum in engineering and biology. We introduce both the principles of Darwinian evolution and the basic evolutionary optimization procedure of evolution strategies. We provide three educational modules in work sheets that can be used by teachers and students to improve their understanding of evolution strategies. The educational module “Optimization of a Milk Carton” shows that the material consumption in producing a milk carton can be minimized using an evolution strategy with a mutative step size control. The use of a standard dice and a pocket calculator enables new milk cartons to be generated, with the offspring having the lowest material consumption becoming the parent of the next generation. The other educational modules deal with the so-called brachistochrone problem. The module “Fastest and Shortest Marble Track” provides a construction plan for a marble track whereby students can experimentally compare the “path of shortest length” with the “path of shortest time”. The EvoBrach software, is used in the module “Various Marble Track Shapes” to compare the running times of a marble on a straight line, a parabola, and a brachistochrone. In conclusion, the introduction to the biomimetic method of evolution strategies and the educational modules should deepen the understanding of both optimization problems and biological evolution. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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17 pages, 843 KiB  
Article
Bio-Inspired Optimization Algorithm Associated with Reinforcement Learning for Multi-Objective Operating Planning in Radioactive Environment
by Shihan Kong, Fang Wu, Hao Liu, Wei Zhang, Jinan Sun, Jian Wang and Junzhi Yu
Biomimetics 2024, 9(7), 438; https://doi.org/10.3390/biomimetics9070438 - 17 Jul 2024
Viewed by 595
Abstract
This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a [...] Read more.
This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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23 pages, 2338 KiB  
Article
A Decomposition-Based Multi-Objective Flying Foxes Optimization Algorithm and Its Applications
by Chen Zhang, Ziyun Song, Yufei Yang, Changsheng Zhang and Ying Guo
Biomimetics 2024, 9(7), 417; https://doi.org/10.3390/biomimetics9070417 - 7 Jul 2024
Viewed by 870
Abstract
The flying foxes optimization (FFO) algorithm stimulated by the strategy used by flying foxes for subsistence in heat wave environments has shown good performance in the single-objective domain. Aiming to explore the effectiveness and benefits of the subsistence strategy used by flying foxes [...] Read more.
The flying foxes optimization (FFO) algorithm stimulated by the strategy used by flying foxes for subsistence in heat wave environments has shown good performance in the single-objective domain. Aiming to explore the effectiveness and benefits of the subsistence strategy used by flying foxes in solving optimization challenges involving multiple objectives, this research proposes a decomposition-based multi-objective flying foxes optimization algorithm (MOEA/D-FFO). It exhibits a great population management strategy, which mainly includes the following features. (1) In order to improve the exploration effectiveness of the flying fox population, a new offspring generation mechanism is introduced to improve the efficiency of exploration of peripheral space by flying fox populations. (2) A new population updating approach is proposed to adjust the neighbor matrices to the corresponding flying fox individuals using the new offspring, with the aim of enhancing the rate of convergence in the population. Through comparison experiments with classical algorithms (MOEA/D, NSGA-II, IBEA) and cutting-edge algorithms (MOEA/D-DYTS, MOEA/D-UR), MOEA/D-FFO achieves more than 11 best results. In addition, the experimental results under different population sizes show that the proposed algorithm is highly adaptable and has good application prospects in optimization problems for engineering applications. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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18 pages, 4494 KiB  
Article
Multi-UAV Cooperative Coverage Search for Various Regions Based on Differential Evolution Algorithm
by Hui Zeng, Lei Tong and Xuewen Xia
Biomimetics 2024, 9(7), 384; https://doi.org/10.3390/biomimetics9070384 - 25 Jun 2024
Viewed by 1112
Abstract
In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the [...] Read more.
In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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44 pages, 18289 KiB  
Article
An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems
by Ruitong Wang, Shuishan Zhang and Guangyu Zou
Biomimetics 2024, 9(6), 361; https://doi.org/10.3390/biomimetics9060361 - 14 Jun 2024
Viewed by 920
Abstract
The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish’s summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed [...] Read more.
The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish’s summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm’s ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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14 pages, 662 KiB  
Article
Gender-Driven English Speech Emotion Recognition with Genetic Algorithm
by Liya Yue, Pei Hu and Jiulong Zhu
Biomimetics 2024, 9(6), 360; https://doi.org/10.3390/biomimetics9060360 - 14 Jun 2024
Viewed by 789
Abstract
Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals [...] Read more.
Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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28 pages, 3369 KiB  
Article
Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm
by Wenjie Tang, Li Cao, Yaodan Chen, Binhe Chen and Yinggao Yue
Biomimetics 2024, 9(5), 298; https://doi.org/10.3390/biomimetics9050298 - 17 May 2024
Cited by 3 | Viewed by 976
Abstract
In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, [...] Read more.
In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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39 pages, 12717 KiB  
Article
Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization
by Kuan Zhang, Yirui He, Yuhang Wang and Changjian Sun
Biomimetics 2024, 9(5), 280; https://doi.org/10.3390/biomimetics9050280 - 8 May 2024
Viewed by 1018
Abstract
The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. [...] Read more.
The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm’s efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm’s escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm’s exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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30 pages, 7655 KiB  
Article
A Sinh–Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems
by Xiong Wang, Yaxin Wei, Zihao Guo, Jihong Wang, Hui Yu and Bin Hu
Biomimetics 2024, 9(5), 271; https://doi.org/10.3390/biomimetics9050271 - 29 Apr 2024
Cited by 2 | Viewed by 1543
Abstract
The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration [...] Read more.
The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh–Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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50 pages, 8922 KiB  
Article
Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation
by Jialing Yan, Gang Hu and Jiulong Zhang
Biomimetics 2024, 9(4), 205; https://doi.org/10.3390/biomimetics9040205 - 28 Mar 2024
Cited by 1 | Viewed by 1217
Abstract
To address the shortcomings of the recently proposed Fick’s Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick’s Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation [...] Read more.
To address the shortcomings of the recently proposed Fick’s Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick’s Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation strategy, interweaving-based comprehensive learning strategy, and seagull update strategy. First, the differential variation strategy is added in the search phase to increase the randomness and expand the search degree of space. Second, by introducing the Gaussian local variation, the search diversity is increased, and the exploration capability and convergence efficiency are further improved. Further, a comprehensive learning strategy that simultaneously updates multiple individual parameters is introduced to improve search diversity and shorten the running time. Finally, the stability of the update is improved by adding a global search mechanism to balance the distribution of molecules on both sides during seagull updates. To test the competitiveness of the algorithms, the exploration and exploitation capability of the proposed FLAS is validated on 23 benchmark functions, and CEC2020 tests. FLAS is compared with other algorithms in seven engineering optimizations such as a reducer, three-bar truss, gear transmission system, piston rod optimization, gas transmission compressor, pressure vessel, and stepped cone pulley. The experimental results verify that FLAS can effectively optimize conventional engineering optimization problems. Finally, the engineering applicability of the FLAS algorithm is further highlighted by analyzing the results of parameter estimation for the solar PV model. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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24 pages, 6054 KiB  
Article
Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm
by Yi Zhang and Yangkun Zhou
Biomimetics 2024, 9(3), 138; https://doi.org/10.3390/biomimetics9030138 - 23 Feb 2024
Viewed by 1585
Abstract
In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing [...] Read more.
In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm’s search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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41 pages, 7627 KiB  
Article
Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems
by Marie Hubálovská, Štěpán Hubálovský and Pavel Trojovský
Biomimetics 2024, 9(3), 137; https://doi.org/10.3390/biomimetics9030137 - 23 Feb 2024
Cited by 4 | Viewed by 1953
Abstract
This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, [...] Read more.
This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA’s ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA’s superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA’s effectiveness in real-world optimization tasks. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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26 pages, 4428 KiB  
Article
A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems
by Nengxian Liu, Jeng-Shyang Pan, Genggeng Liu, Mingjian Fu, Yanyan Kong and Pei Hu
Biomimetics 2024, 9(2), 115; https://doi.org/10.3390/biomimetics9020115 - 15 Feb 2024
Cited by 1 | Viewed by 1431
Abstract
There are a lot of multi-objective optimization problems (MOPs) in the real world, and many multi-objective evolutionary algorithms (MOEAs) have been presented to solve MOPs. However, obtaining non-dominated solutions that trade off convergence and diversity remains a major challenge for a MOEA. To [...] Read more.
There are a lot of multi-objective optimization problems (MOPs) in the real world, and many multi-objective evolutionary algorithms (MOEAs) have been presented to solve MOPs. However, obtaining non-dominated solutions that trade off convergence and diversity remains a major challenge for a MOEA. To solve this problem, this paper designs an efficient multi-objective sine cosine algorithm based on a competitive mechanism (CMOSCA). In the CMOSCA, the ranking relies on non-dominated sorting, and the crowding distance rank is utilized to choose the outstanding agents, which are employed to guide the evolution of the SCA. Furthermore, a competitive mechanism stemming from the shift-based density estimation approach is adopted to devise a new position updating operator for creating offspring agents. In each competition, two agents are randomly selected from the outstanding agents, and the winner of the competition is integrated into the position update scheme of the SCA. The performance of our proposed CMOSCA was first verified on three benchmark suites (i.e., DTLZ, WFG, and ZDT) with diversity characteristics and compared with several MOEAs. The experimental results indicated that the CMOSCA can obtain a Pareto-optimal front with better convergence and diversity. Finally, the CMOSCA was applied to deal with several engineering design problems taken from the literature, and the statistical results demonstrated that the CMOSCA is an efficient and effective approach for engineering design problems. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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19 pages, 3830 KiB  
Article
A Crisscross-Strategy-Boosted Water Flow Optimizer for Global Optimization and Oil Reservoir Production
by Zongzheng Zhao and Shunshe Luo
Biomimetics 2024, 9(1), 20; https://doi.org/10.3390/biomimetics9010020 - 2 Jan 2024
Cited by 2 | Viewed by 1238
Abstract
The growing intricacies in engineering, energy, and geology pose substantial challenges for decision makers, demanding efficient solutions for real-world production. The water flow optimizer (WFO) is an advanced metaheuristic algorithm proposed in 2021, but it still faces the challenge of falling into local [...] Read more.
The growing intricacies in engineering, energy, and geology pose substantial challenges for decision makers, demanding efficient solutions for real-world production. The water flow optimizer (WFO) is an advanced metaheuristic algorithm proposed in 2021, but it still faces the challenge of falling into local optima. In order to adapt WFO more effectively to specific domains and address optimization problems more efficiently, this paper introduces an enhanced water flow optimizer (CCWFO) designed to enhance the convergence speed and accuracy of the algorithm by integrating a cross-search strategy. Comparative experiments, conducted on the CEC2017 benchmarks, illustrate the superior global optimization capability of CCWFO compared to other metaheuristic algorithms. The application of CCWFO to the production optimization of a three-channel reservoir model is explored, with a specific focus on a comparative analysis against several classical evolutionary algorithms. The experimental findings reveal that CCWFO achieves a higher net present value (NPV) within the same limited number of evaluations, establishing itself as a compelling alternative for reservoir production optimization. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)
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