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Biomimetics
Special Issues
Exploration of Bio-Inspired Computing
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Exploration of Bio-Inspired Computing

A special issue of Biomimetics (ISSN 2313-7673).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1691

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
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 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,” 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.

We hope this Special Issue serves as a high-quality platform for researchers and engineers to exchange knowledge and share technology, 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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35 pages, 8735 KiB  
Article
ADVCSO: Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization for Combinatorial Optimization Problems
by Kunwei Wu, Liangshun Wang and Mingming Liu
Biomimetics 2025, 10(5), 303; https://doi.org/10.3390/biomimetics10050303 - 9 May 2025
Viewed by 161
Abstract
High-dimensional complex optimization problems are pervasive in engineering and scientific computing, yet conventional algorithms struggle to meet collaborative optimization requirements due to computational complexity. While Chicken Swarm Optimization (CSO) demonstrates an intuitive understanding and straightforward implementation for low-dimensional problems, it suffers from limitations [...] Read more.
High-dimensional complex optimization problems are pervasive in engineering and scientific computing, yet conventional algorithms struggle to meet collaborative optimization requirements due to computational complexity. While Chicken Swarm Optimization (CSO) demonstrates an intuitive understanding and straightforward implementation for low-dimensional problems, it suffers from limitations including a low convergence precision, uneven initial solution distribution, and premature convergence. This study proposes an Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization (ADVCSO) algorithm. First, to address the uneven initial solution distribution in the original algorithm, we design an elite perturbation initialization strategy based on good point sets, combining low-discrepancy sequences with Gaussian perturbations to significantly improve the search space coverage. Second, targeting the exploration–exploitation imbalance caused by fixed role proportions, a dynamic role allocation mechanism is developed, integrating cosine annealing strategies to adaptively regulate flock proportions and update cycles, thereby enhancing exploration efficiency. Finally, to mitigate the premature convergence induced by single update rules, hybrid mutation strategies are introduced through phased mutation operators and elite dimension inheritance mechanisms, effectively reducing premature convergence risks. Experiments demonstrate that the ADVCSO significantly outperforms state-of-the-art algorithms on 27 of 29 CEC2017 benchmark functions, achieving a 2–3 orders of magnitude improvement in convergence precision over basic CSO. In complex composite scenarios, its convergence accuracy approaches that of the championship algorithm JADE within a 10−2 magnitude difference. For collaborative multi-subproblem optimization, the ADVCSO exhibits a superior performance in both Multiple Traveling Salesman Problems (MTSPs) and Multiple Knapsack Problems (MKPs), reducing the maximum path length in MTSPs by 6.0% to 358.27 units while enhancing the MKP optimal solution success rate by 62.5%. The proposed algorithm demonstrates an exceptional performance in combinatorial optimization and holds a significant engineering application value. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing)
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17 pages, 3452 KiB  
Article
A Categorical Model of General Consciousness
by Yinsheng Zhang
Biomimetics 2025, 10(4), 241; https://doi.org/10.3390/biomimetics10040241 - 14 Apr 2025
Viewed by 625
Abstract
Consciousness is liable to not be defined in scientific research, because it is an object of study in philosophy too, which actually hinders the integration of research on a large scale. The present study attempts to define consciousness with mathematical approaches by including [...] Read more.
Consciousness is liable to not be defined in scientific research, because it is an object of study in philosophy too, which actually hinders the integration of research on a large scale. The present study attempts to define consciousness with mathematical approaches by including the common meaning of consciousness across multiple disciplines. By extracting the essential characteristics of consciousness—transitivity—a categorical model of consciousness is established. This model is used to obtain three layers of categories, namely objects, materials as reflex units, and consciousness per se in homomorphism. The model forms a framework that functional neurons or AI (biomimetic) parts can be treated as variables, functions or local solutions of the model. Consequently, consciousness is quantified algebraically, which helps determining and evaluating consciousness with views that integrate nature and artifacts. Current consciousness theories and computation theories are analyzed to support the model. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing)
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19 pages, 592 KiB  
Article
A Reinforced, Event-Driven, and Attention-Based Convolution Spiking Neural Network for Multivariate Time Series Prediction
by Ying Li, Xikang Guan, Wenwei Yue, Yongsheng Huang, Bin Zhang and Peibo Duan
Biomimetics 2025, 10(4), 240; https://doi.org/10.3390/biomimetics10040240 - 13 Apr 2025
Viewed by 276
Abstract
Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time series (MTS) remains to be addressed. This paper proposes a reinforced, [...] Read more.
Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time series (MTS) remains to be addressed. This paper proposes a reinforced, event-driven, and attention-based convolution SNN model (REAT-CSNN) with three novel features. First, a joint Gramian Angular Field and Rate (GAFR) coding scheme is proposed to convert MTS into spike images, preserving the inherent features in MTS, such as the temporal patterns and spatio-temporal correlations between time series. Second, an advanced LIF-pooling strategy is developed, which is then theoretically and empirically proved to be effective in preserving more features from the regions of interest in spike images than average-pooling strategies. Third, a convolutional block attention mechanism (CBAM) is redesigned to support spike-based input, enhancing event-driven characteristics in weighting operations while maintaining outstanding capability to capture the information encoded in spike images. Experiments on multiple MTS data sets, such as stocks and PM2.5 data sets, demonstrate that our model rivals, and even surpasses, some CNN- and RNN-based techniques, with up to 3% better performance, while consuming significantly less energy. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing)
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