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Exploration of Bio-Inspired Computing: 2nd Edition

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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 6040

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Prof. Dr. Changsheng Zhang

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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

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Guest Editor

School of Computer Science and Engineering, Suzhou University of Technology, Suzhou 215500, China
Interests: multi-objective optimization; large-scale optimization; evolutionary neural architecture search; planning strategies
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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 250 words) can be sent to the Editorial Office for assessment.

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 (8 papers)

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Research

41 pages, 12041 KB

Open AccessArticle

FBCA: Flexible Besiege and Conquer Algorithm for Multi-Layer Perceptron Optimization Problems

by Shuxin Guo, Chenxu Guo and Jianhua Jiang

Biomimetics 2025, 10(11), 787; https://doi.org/10.3390/biomimetics10110787 - 19 Nov 2025

Viewed by 446

Abstract

A Multi-Layer Perceptron (MLP), as the basic structure of neural networks, is an important component of various deep learning models such as CNNs, RNNs, and Transformers. Nevertheless, MLP training faces significant challenges, with a large number of saddle points and local minima in its non-convex optimization space, which can easily lead to gradient vanishing and premature convergence. Compared with traditional heuristic algorithms relying on a population-based parallel search, such as GA, GWO, DE, etc., the Besiege and Conquer Algorithm (BCA) employs a one-spot update strategy that provides a certain level of global optimization capability but exhibits clear limitations in search flexibility. Specifically, it lacks fast detection, fast adaptation, and fast convergence. First, the fixed sinusoidal amplitude limits the accuracy of fast detection in complex regions. Second, the combination of a random location and fixed perturbation range limits the fast adaptation of global convergence. Finally, the lack of a hierarchical adjustment under a single parameter (BCB) hinders the dynamic transition from exploration to exploitation, resulting in slow convergence. To address these limitations, this paper proposes a Flexible Besiege and Conquer Algorithm (FBCA), which improves search flexibility and convergence capability through three new mechanisms: (1) the sine-guided soft asymmetric Gaussian perturbation mechanism enhances local micro-exploration, thereby achieving a fast detection response near the global optimum; (2) the exponentially modulated spiral perturbation mechanism adopts an exponential spiral factor for fast adaptation of global convergence; and (3) the nonlinear cognitive coefficient-driven velocity update mechanism improves the convergence performance, realizing a more balanced exploration–exploitation process. In the IEEE CEC 2017 benchmark function test, FBCA ranked first in the comprehensive comparison with 12 state-of-the-art algorithms, with a win rate of 62% over BCA in 100-dimensional problems. It also achieved the best performance in six MLP optimization problems, showing excellent convergence accuracy and robustness, proving its excellent global optimization ability in complex nonlinear MLP optimization training. It demonstrates its application value and potential in optimizing neural networks and deep learning models. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)

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47 pages, 12120 KB

Open AccessArticle

Multi-Strategy Improved POA for Global Optimization Problems and 3D UAV Path Planning

by Rui Zhang, Jingbo Zhan and Jianfeng Wang

Biomimetics 2025, 10(11), 760; https://doi.org/10.3390/biomimetics10110760 - 11 Nov 2025

Viewed by 485

Abstract

With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of drone mission execution. However, most existing drone path planning algorithms suffer from issues such as requiring extensive interactive information or being prone to getting stuck in local optima. This study introduces a multi-strategy enhanced Pelican Optimization Algorithm (MIPOA) tailored for UAV path planning. To improve the quality of the initial population, a hybrid initialization approach combining low-discrepancy sequences with heuristic refinement is developed. The low-discrepancy component promotes a more uniform distribution across the search space, while the heuristic mechanism enhances the fitness of selected individuals and reduces redundant exploration. Furthermore, a subgroup mean-guided updating strategy is designed to accelerate convergence toward the global optimum. To maintain exploration ability, a random reinitialization boundary mechanism is incorporated, effectively preventing premature convergence. To validate the algorithm’s performance, MIPOA is compared with eleven benchmark metaheuristics on the CEC2017 test suite, and statistical analyses confirm its superior optimization capability. Finally, MIPOA is applied to 3D UAV path planning under four threat scenarios in a realistic environment, demonstrating robust adaptability and achieving successful mission completion. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)

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44 pages, 49738 KB

Open AccessArticle

A Hybrid SAO and RIME Optimizer for Global Optimization and Cloud Task Scheduling

by Ming Zhu, Jing Li and Xiao Yang

Biomimetics 2025, 10(10), 690; https://doi.org/10.3390/biomimetics10100690 - 13 Oct 2025

Viewed by 678

Abstract

In a global industrial landscape where the digital economy accounts for over 40% of total output, cloud computing technology is reshaping business models at a compound annual growth rate of 19%. This trend has led to an increasing number of cloud computing tasks requiring timely processing. However, most computational tasks are latency-sensitive and cannot tolerate significant delays. This has led to the urgent need for researchers to address the challenge of effectively scheduling cloud computing tasks. This paper proposes a hybrid SAO and RIME optimizer (HSAO) for global optimization and cloud task scheduling problems. First, population initialization based on ecological niche differentiation is proposed to enhance the initial population quality of SAO, enabling it to better explore the solution space. Then, the introduction of the soft frost search strategy and hard frost piercing mechanism from the RIME optimization algorithm enables the algorithm to better escape local optima and accelerate its convergence. Additionally, a population-based collaborative boundary control method is proposed to handle outlier individuals, preventing them from clustering at the boundary and enabling more effective exploration of the solution space. To evaluate the effectiveness of the proposed algorithm, we compared it with 11 other algorithms using the IEEE CEC2017 test set and assessed the differences through statistical analysis. Experimental data demonstrate that the HSAO algorithm exhibits significant advantages. Furthermore, to validate its practical applicability, we applied HSAO to real-world cloud computing task scheduling problems, achieving excellent results and successfully completing the scheduling planning of cloud computing tasks. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)

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39 pages, 1466 KB

Open AccessArticle

Empirical Evaluation of an Elitist Replacement Strategy for Differential Evolution with Micro-Populations

by Irving Luna-Ortiz, Alejandro Rodríguez-Molina, Miguel Gabriel Villarreal-Cervantes, Mario Aldape-Pérez, Alam Gabriel Rojas-López and Jesús Aldo Paredes-Ballesteros

Biomimetics 2025, 10(10), 685; https://doi.org/10.3390/biomimetics10100685 - 12 Oct 2025

Viewed by 502

Abstract

This paper introduces a variant of differential evolution with micro-populations, called

μ

This paper introduces a variant of differential evolution with micro-populations, called

μ

-DE-ERM, which incorporates a periodic elitist replacement mechanism with the aim of preserving diversity without the need to measure it explicitly. The proposed algorithm is designed for scenarios with reduced evaluation budgets, where efficiency and convergence stability are critical. Its performance is evaluated on CEC 2005 and CEC 2017 benchmark suites, covering unimodal, multimodal, hybrid, and composition functions, as well as on two real-world engineering problems: the identification of dynamic parameters and the tuning of a PID controller for a one-degree-of-freedom robotic manipulator. The comparative analysis shows that

μ

-DE-ERM achieves competitive or superior results against its predecessors DE and

μ

-DE, and remains effective when contrasted with advanced algorithms such as L-SHADE and RuGA. Furthermore, additional comparisons with algorithms with competitive replacement mechanisms,

μ

-DE-Cauchy and

μ

-DE-Shrink, confirm the robustness of the proposal in real applications, particularly under strict computational constraints. These findings support

μ

-DE-ERM as a practical and efficient alternative for optimization problems in resource-limited environments, delivering reliable solutions at low computational cost. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)

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19 pages, 2069 KB

Open AccessArticle

Learning Guided Binary PSO Algorithm for Feature Selection and Reconstruction of Ultrasound Contrast Images in Endometrial Region Detection

by Zihao Zhang, Yongjun Liu, Haitong Zhao, Yu Zhou, Yifei Xu and Zhengyu Li

Biomimetics 2025, 10(9), 567; https://doi.org/10.3390/biomimetics10090567 - 25 Aug 2025

Viewed by 817

Abstract

Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis. Full article

(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 2nd Edition)

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30 pages, 2890 KB

Open AccessArticle

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

Cited by 1 | Viewed by 656

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 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 KB

Open AccessArticle

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 988

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 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 KB

Open AccessArticle

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 1017

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 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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