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Biomimetics
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Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition
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Advances in Swarm Intelligence Optimization Algorithms and 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 October 2025 | Viewed by 1857

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. Gang Hu

E-Mail Website
Guest Editor
Department of Applied Mathematics, Xi’an University of Technology, Xi’an, China
Interests: metaheuristic algorithms; computing intelligence; artificial intelligence; complex optimization systems; CAD/CAM; image processing and analysis; path planning; multilevel image segmentation; feature selection; Genghis Khan Shark Optimizer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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

This Special Issue, "Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition", aims to consolidate and showcase the latest breakthroughs and achievements in this burgeoning field. It serves as a platform for interdisciplinary research, fostering collaboration among scholars from diverse backgrounds who are exploring the potential of SIOAs for engineering applications. We invite researchers to submit their original contributions that delve into the theoretical foundations, algorithmic innovations, and practical applications of SIOAs, with a focus on addressing specific challenges and advancing the state of the art of the field.

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

  • Novel SIOAs: The development of new swarm intelligence optimization algorithms, including those inspired by unique swarm behaviors or innovative mechanisms for enhancing exploration, exploitation, and convergence.
  • Hybridization and Integration: Studies exploring the integration of SIOAs with other optimization techniques, machine learning algorithms, or heuristic methods to create hybrid optimization frameworks that leverage the strengths of each approach.
  • Theoretical Analysis: In-depth analyses of the mathematical properties, convergence behavior, and complexity of SIOAs, providing insights into their performance and limitations.
  • Parameter Tuning and Adaptation: Research on adaptive parameter control strategies for SIOAs, aimed at enhancing their robustness, versatility, and performance across different problem domains.
  • High-Dimensional and Complex Problems: Applications of SIOAs to tackle high-dimensional, multimodal, dynamic, and noisy optimization problems, demonstrating their effectiveness in real-world contexts.
  • Benchmarking and Comparative Studies: Comparative evaluations of SIOAs using standard and novel benchmark functions, highlighting their strengths and weaknesses relative to other optimization techniques.
  • Engineering Applications: Case studies showcasing the successful application of SIOAs in solving engineering problems, such as design optimization, production scheduling, network routing, and control systems.

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

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

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

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

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Related Special Issue

Published Papers (5 papers)

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Research

20 pages, 7815 KiB  
Article
An Enhanced Snow Geese Optimizer Integrating Multiple Strategies for Numerical Optimization
by Baoqi Zhao, Yu Fang and Tianyi Chen
Biomimetics 2025, 10(6), 388; https://doi.org/10.3390/biomimetics10060388 - 11 Jun 2025
Viewed by 206
Abstract
An enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the [...] Read more.
An enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the search strategy to balance the exploitation and exploration capabilities. Second, a dominant group guidance strategy is introduced to improve the population quality. Finally, a dominant stochastic difference search strategy is designed to enrich the population diversity and help it escape from the local optimum by co-directing effects in multiple directions. Ablation experiments were performed on the CEC2017 test set to illustrate the improvement mechanism and the degree of compatibility of their improved strategies. The proposed ESGA with a highly cited algorithm and the powerful improved algorithm are compared on the CEC2022 test suite, and the experimental results confirm that the ESGA outperforms the compared algorithms. Finally, the ability of the ESGA to solve complex problems is further highlighted by solving the robot path planning problem. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)
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40 pages, 8848 KiB  
Article
Three Strategies Enhance the Bionic Coati Optimization Algorithm for Global Optimization and Feature Selection Problems
by Qingzheng Cao, Shuqi Yuan and Yi Fang
Biomimetics 2025, 10(6), 380; https://doi.org/10.3390/biomimetics10060380 - 7 Jun 2025
Viewed by 247
Abstract
With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training’s computational cost and impair generalization ability. To [...] Read more.
With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training’s computational cost and impair generalization ability. To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. To address the bionic COA’s inadequate global search performance in feature selection (FS) problems, leading to lower classification accuracy, an adaptive search strategy is introduced. This strategy combines individual learning capability and the learnability of disparities, enhancing global exploration. For the imbalance between the exploration and exploitation phases in the bionic COA algorithm when solving FS problems, which often traps it in suboptimal feature subsets, a balancing factor is proposed. By integrating phase control and dynamic adjustability, a good balance between the two phases is achieved, reducing the likelihood of getting stuck in suboptimal subsets. Additionally, to counter the bionic COA’s insufficient local exploitation performance in FS problems, increasing classification error rates, a centroid guidance strategy is presented. By combining population centroid guidance and fractional-order historical memory, the algorithm lowers the classification error rate of feature subsets and speeds up convergence. The bionic ABCCOA algorithm was tested on the CEC2020 test functions and engineering problem, achieving an over 90% optimization success rate and faster convergence, confirming its efficiency. Applied to 27 FS problems, it outperformed comparative algorithms in best, average, and worst fitness function values, classification accuracy, feature subset size, and running time, proving it an efficient and robust FS algorithm. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)
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34 pages, 10238 KiB  
Article
An Improved Northern Goshawk Optimization Algorithm for Mural Image Segmentation
by Jianfeng Wang, Zuowen Bao and Hao Dong
Biomimetics 2025, 10(6), 373; https://doi.org/10.3390/biomimetics10060373 - 5 Jun 2025
Viewed by 286
Abstract
In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation quality. To alleviate the aforementioned issues, this paper proposes a mural [...] Read more.
In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation quality. To alleviate the aforementioned issues, this paper proposes a mural image segmentation algorithm based on OPBNGO by integrating the Northern Goshawk Optimization (NGO) algorithm with the off-center learning strategy, partitioned learning strategy, and Bernstein-weighted learning strategy. In OPBNGO, firstly, the off-center learning strategy is proposed, which effectively improves the global search ability of the algorithm by utilizing biased center individuals. Secondly, the partitioned learning strategy is introduced, which achieves a better balance between the exploration and development phases by applying diverse learning methods to the population. Finally, the Bernstein-weighted learning strategy is proposed, which effectively improves the algorithm’s development performance. Subsequently, the OPBNGO algorithm is applied to solve the image segmentation problem for eight mural images. Experimental results show that it achieves a winning rate of over 96.87% in terms of fitness function value, achieves a winning rate of over 93.75% in terms of FSIM, SSIM, and PSNR metrics, and can be considered a promising mural image segmentation algorithm. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)
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21 pages, 3019 KiB  
Article
IPO: An Improved Parrot Optimizer for Global Optimization and Multilayer Perceptron Classification Problems
by Fang Li, Congteng Dai, Abdelazim G. Hussien and Rong Zheng
Biomimetics 2025, 10(6), 358; https://doi.org/10.3390/biomimetics10060358 - 2 Jun 2025
Viewed by 370
Abstract
The Parrot Optimizer (PO) is a new optimization algorithm based on the behaviors of trained Pyrrhura Molinae parrots. In this paper, an improved PO (IPO) is proposed for solving global optimization problems and training the multilayer perceptron. The basic PO is enhanced by [...] Read more.
The Parrot Optimizer (PO) is a new optimization algorithm based on the behaviors of trained Pyrrhura Molinae parrots. In this paper, an improved PO (IPO) is proposed for solving global optimization problems and training the multilayer perceptron. The basic PO is enhanced by using three improvements, which are aerial search strategy, modified staying behavior, and improved communicating behavior. The aerial search strategy is derived from Arctic Puffin Optimization and is employed to enhance the exploration ability of PO. The staying behavior and communicating behavior of PO are modified using random movement and roulette fitness–distance balance selection methods to achieve a better balance between exploration and exploitation. To evaluate the optimization performance of the proposed IPO, twelve CEC2022 test functions and five standard classification datasets are selected for the experimental tests. The results between IPO and the other six well-known optimization algorithms show that IPO has superior performance for solving complex global optimization problems. The results between IPO and the other six well-known optimization algorithms show that IPO has superior performance for solving complex global optimization problems. In addition, IPO has been applied to optimize a multilayer perceptron model for classifying the oral English teaching quality evaluation dataset. An MLP model with a 10-21-3 structure is constructed for the classification of evaluation outcomes. The results show that IPO-MLP outperforms other algorithms with the highest classification accuracy of 88.33%, which proves the effectiveness of the developed method. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)
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41 pages, 10525 KiB  
Article
An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation
by Xinyu Cai and Chaoyong Zhang
Biomimetics 2025, 10(5), 260; https://doi.org/10.3390/biomimetics10050260 - 23 Apr 2025
Cited by 1 | Viewed by 353
Abstract
In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based [...] Read more.
In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced search efficiency, and hindrance of comprehensive exploration of the solution space. To address the shortcomings of the DCS algorithm, this paper proposes a multi-strategy differentiated creative search (MSDCS) based on the collaborative development mechanism and population evaluation strategy. First, this paper proposes a collaborative development mechanism that organically integrates the estimation distribution algorithm and DCS to compensate for the shortcomings of the DCS algorithm’s insufficient exploration ability and its tendency to fall into local optimums through the guiding effect of dominant populations, and to improve the quality of the DCS algorithm’s search efficiency and solution at the same time. Secondly, a new population evaluation strategy is proposed to realize the coordinated transition between exploitation and exploration through the comprehensive evaluation of fitness and distance. Finally, a linear population size reduction strategy is incorporated into DCS, which significantly improves the overall performance of the algorithm by maintaining a large population size at the initial stage to enhance the exploration capability and extensive search of the solution space, and then gradually decreasing the population size at the later stage to enhance the exploitation capability. A series of validations was conducted on the CEC2018 test set, and the experimental results were analyzed using the Friedman test and Wilcoxon rank sum test. The results show the superior performance of MSDCS in terms of convergence speed, stability, and global optimization. In addition, MSDCS is successfully applied to several engineering constrained optimization problems. In all cases, MSDCS outperforms the basic DCS algorithm with fast convergence and strong robustness, emphasizing its superior efficacy in practical applications. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence Optimization Algorithms and Applications: 2nd Edition)
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