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Biomimetics
Special Issues
Exploration of Bio-Inspired Computing: 2nd Edition
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Exploration of Bio-Inspired Computing: 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: 20 December 2025 | Viewed by 1038

Special Issue Editors

Prof. Dr. Changsheng Zhang

E-Mail Website
Guest Editor
Software College, Northeastern University, Shenyang, China
Interests: evolutionary computing; computational intelligence; new power systems; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Haitong Zhao

E-Mail Website
Guest Editor
Institute of Computer Science and Engineering, Changshu Institute of Technology, Changshu, China
Interests: multi-objective optimization; large-scale optimization; evolutionary neural architecture search; planning strategies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today’s rapidly evolving information technology era, bio-inspired computing, which mimics the behavior and evolutionary mechanisms found in nature, has demonstrated its unique advantages in solving complex problems. This approach provides innovative frameworks and optimization strategies that are capable of addressing the challenges of diversity and complexity emerging in fields such as data processing, automated design, dynamic optimization, and machine learning. This Special Issue, “Exploration of Bio-Inspired Computing: 2nd Edition” aims to bring together the latest academic and industrial research advancements in bio-inspired computing, exploring the future directions of this field.

We invite articles covering innovations in bio-inspired algorithms, application explorations, theoretical analyses, and interdisciplinary applications. Topics of interest include, but are not limited to, evolutionary computing, neural networks, ant colony optimization, immune algorithms, swarm intelligence, deep learning optimization, neural architecture search, and more. Contributions involving the application of bio-inspired computing in fields such as healthcare, bioinformatics, smart cities, industrial engineering, and intelligent manufacturing are also welcome.

This Special Issue aims to serve as a high-quality platform for researchers and engineers to exchange knowledge and share technology developments, inspiring further innovation and collectively advancing the broader application and in-depth development of bio-inspired computing.

Prof. Dr. Changsheng Zhang
Dr. Haitong Zhao
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

  • bio-inspired algorithms
  • evolutionary computation
  • swarm intelligence
  • optimization techniques
  • complex problem solving
  • artificial intelligence in bioinformatics
  • genetic algorithms
  • ant colony optimization
  • computational intelligence
  • machine learning applications
  • nature-inspired design

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Published Papers (3 papers)

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30 pages, 2890 KiB  
Article
A Transfer Function-Based Binary Version of Improved Pied Kingfisher Optimizer for Solving the Uncapacitated Facility Location Problem
by Ayşe Beşkirli
Biomimetics 2025, 10(8), 526; https://doi.org/10.3390/biomimetics10080526 - 12 Aug 2025
Viewed by 204
Abstract
In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of [...] Read more.
In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In addition, performance improvement was realized by adding the Levy flight strategy to BinPKO, and this improved method was named BinIPKO. The experimental results show that the TF1 transfer function for BinIPKO performs very well on all problems in terms of both best and mean solution values. The TF2 transfer function performed efficiently on most Cap problems, ranking second only to TF1. Although the other transfer functions provided competitive solutions in some Cap problems, they lagged behind TF1 and TF2 in terms of overall performance. In addition, the performance of BinIPKO was also compared with the well-known PSO and GWO algorithms in the literature, as well as the recently proposed APO and EEFO algorithms, and it was found that BinIPKO performs well overall. In line with this information, it is seen that the IPKO algorithm, especially when used with the TF1 transfer function, provides an effective alternative for UFLP. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)
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25 pages, 7802 KiB  
Article
A Hybrid Ensemble Equilibrium Optimizer Gene Selection Algorithm for Microarray Data
by Peng Su, Yuxin Zhao, Xiaobo Li, Zhendi Ma and Hui Wang
Biomimetics 2025, 10(8), 523; https://doi.org/10.3390/biomimetics10080523 - 10 Aug 2025
Viewed by 351
Abstract
As modern medical technology advances, the utilization of gene expression data has proliferated across diverse domains, particularly in cancer diagnosis and prognosis monitoring. However, gene expression data is often characterized by high dimensionality and a prevalence of redundant and noisy information, prompting the [...] Read more.
As modern medical technology advances, the utilization of gene expression data has proliferated across diverse domains, particularly in cancer diagnosis and prognosis monitoring. However, gene expression data is often characterized by high dimensionality and a prevalence of redundant and noisy information, prompting the need for effective strategies to mitigate issues like the curse of dimensionality and overfitting. This study introduces a novel hybrid ensemble equilibrium optimizer gene selection algorithm in response. In the first stage, a hybrid approach, combining multiple filters and gene correlation-based methods, is used to select an optimal subset of genes, which is achieved by evaluating the redundancy and complementary relationships among genes to obtain a subset with maximal information content. In the second stage, an equilibrium optimizer algorithm incorporating Gaussian Barebone and a novel gene pruning strategy is employed to further search for the optimal gene subset within the candidate gene space selected in the first stage. To demonstrate the superiority of the proposed method, it was compared with nine feature selection techniques on 15 datasets. The results indicate that the ensemble filtering method in the first stage exhibits strong stability and effectively reduces the search space of the gene selection algorithms. The improved equilibrium optimizer algorithm enhances the prediction accuracy while significantly reducing the number of selected features. These findings highlight the effectiveness of the proposed method as a valuable approach for gene selection. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)
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19 pages, 2415 KiB  
Article
Auto Deep Spiking Neural Network Design Based on an Evolutionary Membrane Algorithm
by Chuang Liu and Haojie Wang
Biomimetics 2025, 10(8), 514; https://doi.org/10.3390/biomimetics10080514 - 6 Aug 2025
Viewed by 301
Abstract
In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the [...] Read more.
In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)
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