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Biomimetics
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Exploration of Bio-Inspired Computing: 3rd Edition
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Exploration of Bio-Inspired Computing: 3rd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 25 January 2027 | Viewed by 2341

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
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
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 of nature, has demonstrated unique advantages in solving complex problems. This approach provides innovative frameworks and optimization strategies capable of addressing the challenges of diversity and complexity emerging across fields such as data processing, automated design, dynamic optimization, and machine learning. This Special Issue, “Exploration of Bio-Inspired Computing: 3rd Edition,” aims to bring together the latest academic and industrial research advances in bio-inspired computing and to explore the future directions of this field.

We invite articles on innovations in bio-inspired algorithms, explorations of applications, 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 that apply bio-inspired computing to 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-anonymized 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 (4 papers)

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Research

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27 pages, 2374 KB  
Article
Enhancing Digital Breast Tomosynthesis Sinograms via Budget-Constrained PSO–Nelder–Mead: A Vision Transformer Assessment
by Luis Fernando Rosas-Ordaz, Estefania Ruiz-Muñoz, Saúl Zapotecas-Martínez, Leopoldo Altamirano-Robles, Raquel Díaz-Hernández and José de Jesús Velázquez Arreola
Biomimetics 2026, 11(8), 586; https://doi.org/10.3390/biomimetics11080586 - 17 Aug 2026
Viewed by 257
Abstract
Sinogram images represent projection-domain data acquired during digital breast tomosynthesis (DBT), preserving angular information prior to reconstruction. However, their low contrast and noise-related degradation limit their direct use in downstream tasks. This work proposes a budget-constrained hybrid biomimetic optimization framework for projection-domain contrast [...] Read more.
Sinogram images represent projection-domain data acquired during digital breast tomosynthesis (DBT), preserving angular information prior to reconstruction. However, their low contrast and noise-related degradation limit their direct use in downstream tasks. This work proposes a budget-constrained hybrid biomimetic optimization framework for projection-domain contrast enhancement based on the integration of Particle Swarm Optimization (PSO) and the Nelder–Mead (NM) simplex method. The approach combines global exploration with local refinement under a fixed number of objective function evaluations (NFE), enabling fair and computationally efficient comparisons with standalone optimizers. Experiments were conducted on 222 labeled mammographic images (136 benign and 86 malignant), which were transformed into sinograms via the Radon transform. The proposed method achieves competitive performance in terms of PSNR, SSIM, and FSIM, while exhibiting faster convergence and reduced computational cost compared to several state-of-the-art swarm-based approaches. Additionally, the impact of the enhanced sinograms was evaluated through a downstream classification task using a Vision Transformer-based knowledge distillation scheme, demonstrating improved discrimination between benign and malignant cases. These results demonstrate that the proposed budget-constrained hybrid biomimetic strategy provides an effective and computationally efficient solution for contrast enhancement in projection-domain medical imaging. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 3rd Edition)
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35 pages, 4570 KB  
Article
Bounded Adaptive Sensitivity Through Bio-Inspired Digital Hormone Regulation for Emotionally Intelligent UAV Traffic Monitoring
by Mohamed Zaidan, Nafaâ Jabeur, Ahmed Nait Sidi Moh, Tufail Ahmed and Ansar-Ul-Haque Yasar
Biomimetics 2026, 11(7), 472; https://doi.org/10.3390/biomimetics11070472 - 6 Jul 2026
Viewed by 520
Abstract
Recently introduced affect-driven UAV controllers model behavioral sensitivity (α) as a static personality-dependent parameter, overlooking the cumulative influence of prolonged operational context. The Pull–Push Engine (PPE) regulates behavioral responses through bounded temporal integration; however, its effective sensitivity remains fixed during execution, [...] Read more.
Recently introduced affect-driven UAV controllers model behavioral sensitivity (α) as a static personality-dependent parameter, overlooking the cumulative influence of prolonged operational context. The Pull–Push Engine (PPE) regulates behavioral responses through bounded temporal integration; however, its effective sensitivity remains fixed during execution, limiting adaptive evolution under cumulative operational exposure. To overcome this limitation, this paper introduces the Digital Hormone Layer (DHL), a bounded neuroendocrine-inspired regulatory mechanism that dynamically modulates the PPE’s effective sensitivity αeff. In DHL, three scalar hormones inspired by cortisol-, dopamine-, and oxytocin-regulatory motifs accumulate operational context on a medium timescale. In the evaluated scenarios, the behavioral effect is primarily stress-driven, while reward and operator-engagement channels remain architecturally defined but contribute less prominently. Modulation is constrained within a personality envelope by coefficient construction (Personality Preservation Budget (PPB) ρ = 0.20). On emergency events, the DHL-augmented controller responds up to 1.91× faster under multi-stressor exposure relative to the activation-selectivity (EI-Low, α = 0.495) control (95% bootstrap confidence interval [1.70×, 2.14×]). This indicates the advantage arises from the bounded adaptive DHL trajectory, not from a low steady-state α-value. This interpretation is consistent with the implemented per-step hormone-to-sensitivity coupling, observed as an inverse correlation between accumulated stress and effective sensitivity. Mission-final and total in-flight battery consumption are comparable across the single-agent controllers; no battery-efficiency advantage is claimed. A single-equation multi-agent extension shows that increasing the coupling coefficient reduces inter-agent sensitivity distance from 0.00502 (uncoupled, γ = 0) to 0.00243 at γ = 0.50 (95% CI [0.00225, 0.00261]; a 51.6% reduction; one-way ANOVA F = 82.5, p < 0.0001) while both agents remain within the personality envelope, as evidence of bounded inter-agent coupling; system-level multi-agent properties remain to be evaluated. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 3rd Edition)
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19 pages, 10746 KB  
Article
Localization Algorithms for Hearing Devices Influenced by Individual Variability in Ear Acoustics
by Jakeh E. Orr and Yan Gai
Biomimetics 2026, 11(7), 467; https://doi.org/10.3390/biomimetics11070467 - 3 Jul 2026
Viewed by 469
Abstract
Background: Head-related transfer functions (HRTFs) contain time and level cues and may be utilized in automatic algorithms to identify locations of sound, a desirable feature for next-generation hearing devices. Due to substantial variability in individual head sizes and ear acoustics, individualized HRTFs are [...] Read more.
Background: Head-related transfer functions (HRTFs) contain time and level cues and may be utilized in automatic algorithms to identify locations of sound, a desirable feature for next-generation hearing devices. Due to substantial variability in individual head sizes and ear acoustics, individualized HRTFs are expected to provide the best localization results. However, acquiring individualized HRTFs for each user is time-consuming. Methods: This study constructed three binaural and/or monaural algorithms suitable for hearing devices. A linear classifier was trained on HRTF databases from a subset of subjects and used to predict sound locations for other individuals to evaluate cross-subject variability. Results: Using the CIPIC Database, a “two-step” method achieved a horizontal localization error of 1.0° and a vertical error of 30.4° sequentially. With the 3D3A Database, the horizontal and vertical errors were 5.6° and 36.5°, respectively. Both datasets yielded improved accuracy when frontal and rear hemifields were simulated separately, with trends remaining consistent across databases. When subjects were grouped by gender, classifiers trained on women’s HRTFs performed well in predicting men’s localization, whereas classifiers trained on men’s HRTFs resulted in significantly larger errors. Conclusions: These findings offer insights into the localization cues embedded in HRTFs and demonstrate the influences of inter-subject variability for spatial hearing devices. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 3rd Edition)
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Review

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53 pages, 2087 KB  
Review
A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions
by Ceren Baştemur Kaya
Biomimetics 2026, 11(6), 439; https://doi.org/10.3390/biomimetics11060439 - 20 Jun 2026
Viewed by 450
Abstract
Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing [...] Read more.
Due to the rapidly increasing number of studies conducted using SFO in recent years, a comprehensive and systematic review of the existing literature has become necessary. SFO is a bio-inspired metaheuristic optimization algorithm developed based on the sun-tracking behavior of sunflower plants. Owing to its simple mathematical structure and flexible search capability, SFO has been increasingly applied to various engineering and AI problems. This review study presents a systematic and comprehensive analysis of SFO-based studies published in the literature. The literature search was performed using the Scopus database, and a total of 192 studies were included in the final evaluation process. The reviewed studies were classified into eight major application domains, including engineering design, energy systems, machine learning, image processing, communication systems, robotics, forecasting, and multi-objective optimization. In addition, the distributions of standard, hybrid, and modified SFO approaches were comparatively analyzed. The temporal evolution of SFO studies, hybridization tendencies, application diversity, strengths, limitations, and future research directions were also systematically evaluated. The findings indicate that hybrid and modified SFO structures have become increasingly dominant in recent years, particularly in AI and data-driven optimization applications. Overall, this review provides a broad understanding of the current state and future research potential of SFO-based optimization studies. Full article
(This article belongs to the Special Issue Exploration of Bio-Inspired Computing: 3rd Edition)
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