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Biomimetics
Special Issues
Bio-Inspired Machine Learning and Evolutionary Computing
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Bio-Inspired Machine Learning and Evolutionary Computing

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Development of Biomimetic Methodology".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 8844

Special Issue Editor

Dr. Dionisios N. Sotiropoulos

E-Mail Website
Guest Editor
Department of Informatics, University of Piraeus, Karaoli & Dimitriou 80, 18534 Piraeus, Greece
Interests: biologically inspired computing‬; machine learning; pattern recognition; data mining; evolutionary computing; signal processing; digital social networks

Special Issue Information

Dear Colleagues,

The Special Issue Bio-Inspired Machine Learning and Evolutionary Computing explores computational paradigms inspired by natural and biological systems to address complex problems in optimization, learning, and adaptation. Drawing from evolutionary biology, neurodynamics, swarm intelligence, and immune systems, this field develops algorithms that mimic natural processes such as natural selection, neural processing, genetic recombination, and collective behavior. These methods offer flexible and robust approaches to solving high-dimensional, non-linear, and dynamic problems often intractable by conventional techniques. Contributions to this Special Issue should include theoretical advances, algorithmic innovations, hybrid models, and practical applications across domains like robotics, pattern recognition, healthcare, and finance. Emphasis is placed on the integration of biologically plausible mechanisms with modern machine learning frameworks, including deep learning, reinforcement learning, and generative models. This convergence fosters the development of adaptive, explainable, and generalizable systems capable of learning from sparse or noisy data and evolving in changing environments. The Special Issue welcomes novel research and survey articles that contribute to the understanding, design, and application of bio-inspired and evolutionary learning techniques.

Dr. Dionisios N. Sotiropoulos
Guest Editor

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 computing
  • swarm intelligence
  • neural computation
  • genetic algorithms
  • machine learning
  • artificial immune systems
  • reinforcement learning
  • adaptation and self-organization
  • and hybrid intelligent systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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34 pages, 955 KB  
Article
V-CHIMERA: An Immune-Inspired Verified Framework for Organizational Cyber Crisis Response Under Misinformation
by Fahad Alghamdi and Saad Alqithami
Biomimetics 2026, 11(5), 324; https://doi.org/10.3390/biomimetics11050324 - 6 May 2026
Viewed by 578
Abstract
In organizational cyber crises, incident response and official communication form coupled control loops, yet they are usually engineered separately. We present V-CHIMERA (Verified Coupled Human–Information–Machine Incident Response Architecture), a framework for organizational cyber crisis response under misinformation that jointly models cyber state, belief [...] Read more.
In organizational cyber crises, incident response and official communication form coupled control loops, yet they are usually engineered separately. We present V-CHIMERA (Verified Coupled Human–Information–Machine Incident Response Architecture), a framework for organizational cyber crisis response under misinformation that jointly models cyber state, belief dynamics, trust, and communication governance. The framework combines three elements: an explicit cyber–social coupling architecture, a runtime protocol shield for communication safety, and immune-gated coupling (IGC) that uses danger signaling, tolerance thresholds, and immune memory to regulate when social feedback should affect operational response and how strongly counter-messaging should be targeted. Across three representative scenarios—ransomware rumor, outage rumor, and exfiltration scam—and eight seeds per scenario, all shielded policies achieved zero executed protocol violations. Relative to naive coupled control, IGC reduced cyber-harm area under the curve (AUC) by 57.6% in ransomware rumor and 42.6% in outage rumor while also reducing misbelief. Results were scenario-dependent rather than uniformly dominant: in exfiltration scam, a broadcast-only ablation outperformed targeted messaging, showing that targeting can fail when diffusion rapidly crosses community boundaries. Sensitivity analysis further shows that IGC attenuates the brittleness observed under strong coupling and weak moderation. The results suggest that biomimetic regulation is valuable not because coupling always helps, but because it prevents overreaction, clarifies when targeting should be used, and yields safer organizational defaults for misinformation-aware incident response. Full article
(This article belongs to the Special Issue Bio-Inspired Machine Learning and Evolutionary Computing)
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36 pages, 27311 KB  
Article
Multi-Threshold Image Segmentation Based on the Hybrid Strategy Improved Dingo Optimization Algorithm
by Qianqian Zhu, Min Gong, Yijie Wang and Zhengxing Yang
Biomimetics 2026, 11(1), 52; https://doi.org/10.3390/biomimetics11010052 - 8 Jan 2026
Cited by 1 | Viewed by 678
Abstract
This study proposes a Hybrid Strategy Improved Dingo Optimization Algorithm (HSIDOA), designed to address the limitations of the standard DOA in complex optimization tasks, including its tendency to fall into local optima, slow convergence speed, and inefficient boundary search. The HSIDOA integrates a [...] Read more.
This study proposes a Hybrid Strategy Improved Dingo Optimization Algorithm (HSIDOA), designed to address the limitations of the standard DOA in complex optimization tasks, including its tendency to fall into local optima, slow convergence speed, and inefficient boundary search. The HSIDOA integrates a quadratic interpolation search strategy, a horizontal crossover search strategy, and a centroid-based opposition learning boundary-handling mechanism. By enhancing local exploitation, global exploration, and out-of-bounds correction, the algorithm forms an optimization framework that excels in convergence accuracy, speed, and stability. On the CEC2017 (30-dimensional) and CEC2022 (10/20-dimensional) benchmark suites, the HSIDOA achieves significantly superior performance in terms of average fitness, standard deviation, convergence rate, and Friedman test rankings, outperforming seven mainstream algorithms including MLPSO, MELGWO, MHWOA, ALA, HO, RIME, and DOA. The results demonstrate strong robustness and scalability across different dimensional settings. Furthermore, HSIDOA is applied to multi-level threshold image segmentation, where Otsu’s maximum between-class variance is used as the objective function, and PSNR, SSIM, and FSIM serve as evaluation metrics. Experimental results show that HSIDOA consistently achieves the best segmentation quality across four threshold levels (4, 6, 8, and 10 levels). Its convergence curves exhibit rapid decline and early stabilization, with stability surpassing all comparison algorithms. In summary, HSIDOA delivers comprehensive improvements in global exploration capability, local exploitation precision, convergence speed, and high-dimensional robustness. It provides an efficient, stable, and versatile optimization method suitable for both complex numerical optimization and image segmentation tasks. Full article
(This article belongs to the Special Issue Bio-Inspired Machine Learning and Evolutionary Computing)
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33 pages, 3248 KB  
Article
Weibull Parameter Estimation Using Empirical and AI Methods: A Wind Energy Assessment in İzmir
by Bayram Köse
Biomimetics 2025, 10(10), 709; https://doi.org/10.3390/biomimetics10100709 - 20 Oct 2025
Cited by 2 | Viewed by 1474
Abstract
This study evaluates the estimation of Weibull distribution parameters (shape, k; scale, c) for wind speed modeling in wind energy potential assessments. Traditional empirical methods—Justus Moment Method (JEM), Power Density Method (PDM), Energy Pattern Factor Method (EPFM), Lysen Moment Method (LAM), [...] Read more.
This study evaluates the estimation of Weibull distribution parameters (shape, k; scale, c) for wind speed modeling in wind energy potential assessments. Traditional empirical methods—Justus Moment Method (JEM), Power Density Method (PDM), Energy Pattern Factor Method (EPFM), Lysen Moment Method (LAM), and Standard Deviation Empirical Method (SEM)—are compared with advanced artificial intelligence optimization algorithms (AIOAs), including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Sine Cosine Algorithm (SCA), Teaching-Learning-Based Optimization (TLBA), Grey Wolf Optimizer (GWA), Red Fox Algorithm (RFA), and Red Panda Optimization Algorithm (RPA). Using hourly wind speed data from Foça, Urla, Karaburun, and Çeşme in Turkey, the analysis demonstrates that AIOAs, particularly GA, GSA, SCA, TLBA, and GWA, outperform empirical methods, achieving low RMSE (0.0071) and high R2 (0.9755). SEM and LAM perform competitively among empirical methods, while PDM and EPFM show higher errors, highlighting their limitations in complex wind speed distributions. The study also conducts a techno-economic analysis, assessing capacity factors, unit energy costs, and payback periods. Foça and Urla are identified as optimal investment sites due to high energy yields and economic efficiency, whereas Çeşme is unviable due to low production and long payback periods. This research provides a robust framework for Weibull parameter estimation, demonstrating AIOAs’ superior accuracy and offering a decision-support tool for sustainable wind energy investments. Full article
(This article belongs to the Special Issue Bio-Inspired Machine Learning and Evolutionary Computing)
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Review

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35 pages, 7098 KB  
Review
Recent Advances in Optoelectronic Synaptic Devices for Neuromorphic Computing
by Heeseong Jang, Seohyeon Ju, Seeun Lee, Jaewoo Choi, Ungbin Byun, Kyeongjun Min, Maria Rasheed and Sungjun Kim
Biomimetics 2025, 10(9), 584; https://doi.org/10.3390/biomimetics10090584 - 3 Sep 2025
Cited by 5 | Viewed by 5634
Abstract
We explore recent advancements in optoelectronic synaptic devices across four key aspects: mechanisms, materials, synaptic properties, and applications. First, we discuss fundamental working principles, including oxygen vacancy ionization, defect trapping, and heterojunction-based charge modulation, which contribute to synaptic plasticity. Next, we examine the [...] Read more.
We explore recent advancements in optoelectronic synaptic devices across four key aspects: mechanisms, materials, synaptic properties, and applications. First, we discuss fundamental working principles, including oxygen vacancy ionization, defect trapping, and heterojunction-based charge modulation, which contribute to synaptic plasticity. Next, we examine the role of 0D, 1D, and 2D materials in optimizing device performance, focusing on their unique electronic, optical, and mechanical properties. We then analyze synaptic properties such as excitatory post-synaptic current (EPSC), visual adaptation, transition from short-term to long-term plasticity (STP to LTP), nociceptor-inspired responses, and associative learning mechanisms. Finally, we highlight real-world applications, including artificial vision systems, reservoir computing for temporal data processing, adaptive neuromorphic computing for exoplanet detection, and colored image recognition. By consolidating recent developments, this paper provides insights into the potential of optoelectronic synaptic devices for next-generation computing architectures, bridging the gap between optics and neuromorphic engineering. Full article
(This article belongs to the Special Issue Bio-Inspired Machine Learning and Evolutionary Computing)
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