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Biomimetics
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Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition
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Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd 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 July 2025 | Viewed by 3445

Special Issue Editors

Prof. Dr. Heming Jia

E-Mail Website
Guest Editor
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
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 of industrialization, engineering applications are becoming more and more frequent, as are problems associated with such development. To solve these complex real-world problems, a host of optimization algorithms have been proposed, and bio-inspired optimization algorithms account for a large proportion. The literature shows that bio-inspired optimization algorithms that can rapidly converge and escape from local optimal states could solve complex problems, such as non-convex, nonlinear constraints and high-dimensional problems. Due to the high-performance nature of these optimization algorithms, through an exploration and exploitation process, accurate and adequate results can eventually be produced at a low cost.

This Special Issue will compile recent high-quality papers focusing on interdisciplinary research on the optimization algorithm for engineering applications using methods inspired by the dynamic and intelligent behavior of creatures, such as hunting, mating, and other social behaviors. We invite researchers to submit original contributions addressing particularly 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
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 (8 papers)

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Research

29 pages, 35588 KiB  
Article
Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm
by Lin Yang, Qinghua Chen, Junjie Mu, Tangying Liu, Xiaoxiao Li and Shuxiang Cai
Biomimetics 2025, 10(5), 285; https://doi.org/10.3390/biomimetics10050285 - 2 May 2025
Abstract
Ship replenishment path planning is a critical problem in the field of maritime logistics. This study proposes a novel synergistic hybrid optimization algorithm (SHOA) that effectively integrates ant colony optimization (ACO), the Clarke–Wright algorithm (CW), and the genetic algorithm (GA) to solve the [...] Read more.
Ship replenishment path planning is a critical problem in the field of maritime logistics. This study proposes a novel synergistic hybrid optimization algorithm (SHOA) that effectively integrates ant colony optimization (ACO), the Clarke–Wright algorithm (CW), and the genetic algorithm (GA) to solve the capacitated multi-ship replenishment path planning problem (CMSRPPP). The proposed methodology employs a three-stage optimization framework: (1) initial path generation via parallel execution of the CW and ACO; (2) population initialization for the GA by strategically combining optimal solutions from ACO and the CW with randomized solutions; (3) iterative refinement using an enhanced GA featuring an embedded evolutionary reversal operation for local intensification. To evaluate performance, the SHOA is benchmarked against ACO, the GA, the particle swarm optimization algorithm, and the simulated annealing algorithm for the capacitated vehicle routing problem. Finally, the SHOA is applied to diverse CMSRPPP instances, demonstrating high adaptability, robust planning capabilities, and promising practical potential. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
27 pages, 3097 KiB  
Article
An Improved Whale Optimization Algorithm for the Clean Production Transformation of Automotive Body Painting
by Qin Yang, Xinning Li, Teng Yang, Hu Wu and Liwen Zhang
Biomimetics 2025, 10(5), 273; https://doi.org/10.3390/biomimetics10050273 - 28 Apr 2025
Viewed by 139
Abstract
Research on clean production in automotive painting processes is a core component of achieving green manufacturing, addressing environmental regulatory challenges, and advancing sustainable development in the automotive industry by reducing volatile organic compound (VOC) emissions, optimizing resource utilization, and minimizing energy consumption. To [...] Read more.
Research on clean production in automotive painting processes is a core component of achieving green manufacturing, addressing environmental regulatory challenges, and advancing sustainable development in the automotive industry by reducing volatile organic compound (VOC) emissions, optimizing resource utilization, and minimizing energy consumption. To reduce pollutants generated by automotive painting processes and improve coating efficiency, this study proposes a clean production method for automotive body painting based on an improved whale optimization algorithm from the perspective of “low-carbon consumption and emission-reduced production”. A multi-level, multi-objective decision-making model is developed by integrating three dimensions of clean production: material flow (optimizing material costs), energy flow (minimizing painting energy consumption), and environmental emission flow (reducing carbon emissions and processing time). The whale optimization algorithm is enhanced through three key modifications: the incorporation of nonlinear convergence factors, elite opposition-based learning, and dynamic parameter self-adaptation, which are then applied to optimize the automotive painting model. Experimental validation using the painting processes of TJ Corporation’s New Energy Vehicles (NEVs) demonstrates the superiority of the proposed algorithm over the MHWOA, WOA-RBF, and WOA-VMD. Results show that the method achieves a 42.1% increase in coating production efficiency, over 98% exhaust gas purification rate, 18.2% average energy-saving improvement, and 17.9% reduction in manufacturing costs. This green transformation of low-carbon emission-reduction infrastructure in painting processes delivers significant economic and social benefits, positioning it as a sustainable solution for the automotive industry. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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30 pages, 5159 KiB  
Article
Snake Optimization Algorithm Augmented by Adaptive t-Distribution Mixed Mutation and Its Application in Energy Storage System Capacity Optimization
by Yinggao Yue, Li Cao, Changzu Chen, Yaodan Chen and Binhe Chen
Biomimetics 2025, 10(4), 244; https://doi.org/10.3390/biomimetics10040244 - 16 Apr 2025
Viewed by 279
Abstract
To address the drawbacks of the traditional snake optimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation snake optimization strategy is proposed. Initially, Tent-based chaotic mapping and the quasi-reverse learning approach are [...] Read more.
To address the drawbacks of the traditional snake optimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation snake optimization strategy is proposed. Initially, Tent-based chaotic mapping and the quasi-reverse learning approach are utilized to enhance the quality of the initial solution and the population initialization process of the original method. During the evolution stage, a novel adaptive t-distribution mixed mutation foraging strategy is introduced to substitute the original foraging stage method. This strategy perturbs and mutates at the optimal solution position to generate new solutions, thereby improving the algorithm’s ability to escape local optima. The mating mode in the evolution stage is replaced with an opposite-sex attraction mechanism, providing the algorithm with more opportunities for global exploration and exploitation. The improved snake optimization method accelerates convergence and improves accuracy while balancing the algorithm’s local and global exploitation capabilities. The experimental results demonstrate that the improved method outperforms other optimization methods, including the standard snake optimization technique, in terms of solution robustness and accuracy. Additionally, each improvement technique complements and amplifies the effects of the others. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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31 pages, 7050 KiB  
Article
mESC: An Enhanced Escape Algorithm Fusing Multiple Strategies for Engineering Optimization
by Jia Liu, Jianwei Yang and Lele Cui
Biomimetics 2025, 10(4), 232; https://doi.org/10.3390/biomimetics10040232 - 8 Apr 2025
Viewed by 310
Abstract
A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address the challenges of balancing exploration and development stages and low convergence accuracy in the escape algorithm (ESC). Firstly, an adaptive perturbation factor strategy was employed to maintain population [...] Read more.
A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address the challenges of balancing exploration and development stages and low convergence accuracy in the escape algorithm (ESC). Firstly, an adaptive perturbation factor strategy was employed to maintain population diversity. Secondly, introducing a restart mechanism to enhance the exploration capability of mESC. Finally, a dynamic centroid reverse learning strategy was designed to balance local development. In addition, in order to accelerate the global convergence speed, a boundary adjustment strategy based on the elite pool is proposed, which selects elite individuals to replace bad individuals. Comparing mESC with the latest metaheuristic algorithm and high-performance winner algorithm in the CEC2022 testing suite, numerical results confirmed that mESC outperforms other competitors. Finally, the superiority of mESC in handling problems was verified through several classic real-world optimization problems. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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23 pages, 433 KiB  
Article
Performance Guarantees of Recurrent Neural Networks for the Subset Sum Problem
by Zengkai Wang, Weizhi Liao, Youzhen Jin and Zijia Wang
Biomimetics 2025, 10(4), 231; https://doi.org/10.3390/biomimetics10040231 - 8 Apr 2025
Viewed by 298
Abstract
The subset sum problem is a classical NP-hard problem. Various methods have been developed to address this issue, including backtracking techniques, dynamic programming approaches, branch-and-bound strategies, and Monte Carlo methods. In recent years, researchers have proposed several neural network-based methods for solving combinatorial [...] Read more.
The subset sum problem is a classical NP-hard problem. Various methods have been developed to address this issue, including backtracking techniques, dynamic programming approaches, branch-and-bound strategies, and Monte Carlo methods. In recent years, researchers have proposed several neural network-based methods for solving combinatorial optimization problems, which have shown commendable performance. However, there has been limited research on the performance guarantees of recurrent neural networks (RNNs) when applied to the subset sum problem. In this paper, we conduct a novel investigation into the performance guarantees of RNNs to solve the subset sum problem for the first time. A construction method for RNNs is developed to compute both exact and approximate solutions of subset sum problems, and the mathematical model of each hidden layer in RNNs is rigorously defined. Furthermore, the correctness of the proposed RNNs is strictly proven through mathematical reasoning, and their performance is thoroughly analyzed. In particular, we prove wNN≥wOPT(1−ε) mathematically, i.e., the errors between the approximate solutions obtained by the proposed ASS-NN model and the actual optimal solutions are relatively small and highly consistent with theoretical expectations. Finally, the validity of RNNs is verified through a series of examples, where the actual error value of the approximate solution aligns closely with the theoretical error value. Additionally, our research reveals that recurrence relations in dynamic programming can effectively simulate the process of constructing solutions. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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29 pages, 8461 KiB  
Article
Three-Dimensional UAV Path Planning Based on Multi-Strategy Integrated Artificial Protozoa Optimizer
by Qingbin Sun, Xitai Na, Zhihui Feng, Shiji Hai and Jinshuo Shi
Biomimetics 2025, 10(4), 201; https://doi.org/10.3390/biomimetics10040201 - 25 Mar 2025
Viewed by 261
Abstract
Three-dimensional UAV path planning is crucial in practical applications. However, existing metaheuristic algorithms often suffer from slow convergence and susceptibility to becoming trapped in local optima. To address these limitations, this paper proposes a multi-strategy integrated artificial protozoa optimization (IAPO) algorithm for UAV [...] Read more.
Three-dimensional UAV path planning is crucial in practical applications. However, existing metaheuristic algorithms often suffer from slow convergence and susceptibility to becoming trapped in local optima. To address these limitations, this paper proposes a multi-strategy integrated artificial protozoa optimization (IAPO) algorithm for UAV 3D path planning. First, the tent map and refractive opposition-based learning (ROBL) are employed to enhance the diversity and quality of the initial population. Second, in the algorithm’s autotrophic foraging stage, we design a dynamic optimal leadership mechanism, which accelerates the convergence speed while ensuring robust exploration capability. Additionally, during the reproduction phase of the algorithm, we update positions using a Cauchy mutation strategy. Thanks to the heavy-tailed nature of the Cauchy distribution, the algorithm is less likely to become trapped in local optima during exploration, thereby increasing the probability of finding the global optimum. Finally, we incorporate the simulated annealing algorithm into the heterotrophic foraging and reproduction stages, effectively preventing the algorithm from getting trapped in local optima and reducing the impact of inferior solutions on the convergence efficiency. The proposed algorithm is validated through comparative experiments using 12 benchmark functions from the 2022 IEEE Congress on Evolutionary Computation (CEC), outperforming nine common algorithms in terms of convergence speed and optimization accuracy. The experimental results also demonstrate IAPO’s superior performance in generating collision-free and energy-efficient UAV paths across diverse 3D environments. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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37 pages, 7718 KiB  
Article
EDECO: An Enhanced Educational Competition Optimizer for Numerical Optimization Problems
by Wenkai Tang, Shangqing Shi, Zengtong Lu, Mengying Lin and Hao Cheng
Biomimetics 2025, 10(3), 176; https://doi.org/10.3390/biomimetics10030176 - 12 Mar 2025
Viewed by 588
Abstract
The Educational Competition Optimizer (ECO) is a newly proposed human-based metaheuristic algorithm. It derives from the phenomenon of educational competition in society with good performance. However, the basic ECO is constrained by its limited exploitation and exploration abilities when tackling complex optimization problems [...] Read more.
The Educational Competition Optimizer (ECO) is a newly proposed human-based metaheuristic algorithm. It derives from the phenomenon of educational competition in society with good performance. However, the basic ECO is constrained by its limited exploitation and exploration abilities when tackling complex optimization problems and exhibits the drawbacks of premature convergence and diminished population diversity. To this end, this paper proposes an enhanced educational competition optimizer, named EDECO, by incorporating estimation of distribution algorithm and replacing some of the best individual(s) using a dynamic fitness distance balancing strategy. On the one hand, the estimation of distribution algorithm enhances the global exploration ability and improves the population quality by establishing a probabilistic model based on the dominant individuals provided by EDECO, which solves the problem that the algorithm is unable to search the neighborhood of the optimal solution. On the other hand, the dynamic fitness distance balancing strategy increases the convergence speed of the algorithm and balances the exploitation and exploration through an adaptive mechanism. Finally, this paper conducts experiments on the proposed EDECO algorithm with 29 CEC 2017 benchmark functions and compares EDECO with four basic algorithms as well as four advanced improved algorithms. The results show that EDECO indeed achieves significant improvements compared to the basic ECO and other compared algorithms, and performs noticeably better than its competitors. Next, this study applies EDECO to 10 engineering constrained optimization problems, and the experimental results show the significant superiority of EDECO in solving real engineering optimization problems. These findings further support the effectiveness and usefulness of our proposed algorithm in solving complex engineering optimization challenges. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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33 pages, 8058 KiB  
Article
GOHBA: Improved Honey Badger Algorithm for Global Optimization
by Yourui Huang, Sen Lu, Quanzeng Liu, Tao Han and Tingting Li
Biomimetics 2025, 10(2), 92; https://doi.org/10.3390/biomimetics10020092 - 6 Feb 2025
Cited by 1 | Viewed by 998
Abstract
Aiming at the problem that the honey badger algorithm easily falls into local convergence, insufficient global search ability, and low convergence speed, this paper proposes a global optimization honey badger algorithm (Global Optimization HBA) (GOHBA), which improves the search ability of the population, [...] Read more.
Aiming at the problem that the honey badger algorithm easily falls into local convergence, insufficient global search ability, and low convergence speed, this paper proposes a global optimization honey badger algorithm (Global Optimization HBA) (GOHBA), which improves the search ability of the population, with better ability to jump out of the local optimum, faster convergence speed, and better stability. The introduction of Tent chaotic mapping initialization enhances the population diversity and initializes the population quality of the HBA. Replacing the density factor enhances the search range of the algorithm in the entire solution space and avoids premature convergence to a local optimum. The addition of the golden sine strategy enhances the global search capability of the HBA and accelerates the convergence speed. Compared with seven algorithms, the GOHBA achieves the optimal mean value on 14 of the 23 tested functions. On two real-world engineering design problems, the GOHBA was optimal. On three path planning problems, the GOHBA had higher accuracy and faster convergence. The above experimental results show that the performance of the GOHBA is indeed excellent. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)
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