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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 July 2026 | Viewed by 936

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 (2 papers)

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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 154
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 315
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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