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Biomimetics
Special Issues
Bio-Inspired Computation and Its Applications
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Bio-Inspired Computation and Its Applications

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1465

Editor

Dr. Chaoqun Li

E-Mail
Guest Editor
School of Computer Science and Technology, Shandong University, Qingdao 266237, China
Interests: bio-inspired computing; nature-inspired; swarm intelligence; artificial intelligence; machine learning; neural networks

Special Issue Information

Dear Colleagues,

This Special Issue titled “Bio-Inspired Computation and Its Applications” focuses on the synergistic integration of bio-inspired/nature-inspired methodologies (e.g., swarm intelligence, evolutionary algorithms) with deep learning and artificial intelligence techniques, targeting the solution of complex real-world problems. Bio-inspired computation, renowned for its adaptive optimization and global search capabilities, complements the strong feature learning prowess of deep learning, creating hybrid intelligent systems that outperform standalone approaches in handling high-dimensional, non-linear, and dynamic scenarios. We welcome original research and concise reviews covering novel hybrid algorithm design, performance benchmarking, and practical applications in fields such as computer vision, network security, intelligent manufacturing, and data analytics. This SI aims to showcase cutting-edge advancements in the cross-disciplinary domain, foster academic exchanges among researchers, and accelerate the translation of theoretical innovations into industrial and societal solutions.

Dr. Chaoqun Li
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-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 computation
  • nature-inspired algorithms
  • swarm intelligence
  • deep learning
  • heuristic algorithms
  • hybrid intelligent systems
  • machine learning
  • artificial intelligence
  • intelligent optimization
  • application-oriented AI

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

Published Papers (3 papers)

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Research

26 pages, 1280 KB  
Article
Drosophila Optimization Algorithm Based on Chaotic Development Mechanism and Orthogonal Learning Strategy for Reservoir Optimization
by Rong Lv, Guofa Lei, Hanchao Liu, Yuhan Sun, Wenhua Wang and Xuebin Du
Biomimetics 2026, 11(6), 430; https://doi.org/10.3390/biomimetics11060430 (registering DOI) - 17 Jun 2026
Viewed by 254
Abstract
Enhancing oil and gas production performance is essential for maintaining the economic sustainability of petroleum enterprises and meeting the increasing global energy requirements. In this context, subsurface production optimization constitutes a fundamental component of strategic reservoir management, directly affecting critical decisions such as [...] Read more.
Enhancing oil and gas production performance is essential for maintaining the economic sustainability of petroleum enterprises and meeting the increasing global energy requirements. In this context, subsurface production optimization constitutes a fundamental component of strategic reservoir management, directly affecting critical decisions such as well location design and the regulation of operational parameters. Nevertheless, conventional reservoir optimization approaches are frequently constrained by high computational costs and limited optimization effectiveness. To overcome these limitations, evolutionary algorithms have gained considerable attention for addressing complex optimization tasks, owing to their gradient-free nature and strong capability for parallel exploration. This paper proposes a chaotic exploitation orthogonal learning fruit fly optimization algorithm (COFOA) tailored for global optimization and oil and gas production optimization. Specifically, we integrate a chaotic exploitation mechanism and an orthogonal learning strategy to improve the balance between exploration and exploitation. Following the population update in FOA, the chaotic exploitation mechanism is first applied to help the population escape local optima and enhance search efficiency. Subsequently, the orthogonal learning strategy is employed to strengthen the algorithm’s exploitation capability. To evaluate the performance of the improved FOA, extensive experiments were conducted on benchmark functions from IEEE CEC 2017 and IEEE CEC 2022, including ablation studies, scalability tests and comparisons with state-of-the-art algorithms. The results demonstrate that the proposed FOA significantly outperforms competing algorithms in optimizing reservoir production. COFOA demonstrates consistent performance superiority over all compared algorithms in terms of mean NPV. Specifically, it achieves improvements of approximately 2.35% to 16.23% compared with existing methods. Notably, COFOA outperforms strong competitors such as mSCA and BLPSO by 2.35% and 3.81%, respectively, while achieving more significant gains over algorithms such as SCADE (15.31%) and CCMSCSA (16.23%). Even when compared with relatively competitive methods like HGWO and CCMWOA, COFOA still maintains performance improvements of 4.79% and 6.12%, respectively. These results clearly demonstrate the superior optimization capability of COFOA in terms of maximizing NPV under complex reservoir conditions. Full article
(This article belongs to the Special Issue Bio-Inspired Computation and Its Applications)
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21 pages, 2676 KB  
Article
Comprehensive Learning Fungal Growth Optimizer for Numerical Optimization and Reservoir Production Optimization
by Mingyang Gong, Zhenyu Song, Xiaonan Zhang and Yi Tang
Biomimetics 2026, 11(6), 370; https://doi.org/10.3390/biomimetics11060370 - 27 May 2026
Viewed by 315
Abstract
The Fungal Growth Optimizer (FGO) is a nature-inspired metaheuristic that simulates fungal colony behaviors, but its exploitation phase can lose search diversity when guidance is dominated by limited peer or global-best information. In this paper, we propose an enhanced variant called the Comprehensive [...] Read more.
The Fungal Growth Optimizer (FGO) is a nature-inspired metaheuristic that simulates fungal colony behaviors, but its exploitation phase can lose search diversity when guidance is dominated by limited peer or global-best information. In this paper, we propose an enhanced variant called the Comprehensive Learning Fungal Growth Optimizer (CLFGO). We integrate a conditionally activated Comprehensive Learning (CL) strategy into the FGO framework. When a candidate solution stagnates, the strategy constructs a dimension-specific learning exemplar. This mechanism allows each dimension to learn from the personal best of a different peer, extending the original fungal growth model. CLFGO is therefore intended for high-dimensional, multimodal, hybrid, and composition landscapes in which the original FGO is prone to diversity loss, rather than as a universal replacement for all problem classes. This approach improves population diversity and reduces the risk of premature convergence. We evaluated CLFGO on 29 CEC2017 benchmark functions at 30 dimensions against nine metaheuristics under the same maximum-function-evaluation budget. CLFGO achieved the lowest mean error on 21 of 29 functions and attained a Friedman average rank of 1.5517. Furthermore, we applied CLFGO to a reservoir production optimization problem, where it obtained a mean Net Present Value of 9.97×108 USD, outperforming the compared algorithms in both solution accuracy and convergence stability. Full article
(This article belongs to the Special Issue Bio-Inspired Computation and Its Applications)
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19 pages, 4329 KB  
Article
A Crisscross-Enhanced Groupers and Moray Eels Optimization Algorithm: Benchmark Test and Production Optimization
by Yuwei Fan, Zhilin Cheng and Youyou Cheng
Biomimetics 2026, 11(5), 322; https://doi.org/10.3390/biomimetics11050322 - 6 May 2026
Viewed by 503
Abstract
Metaheuristic algorithms can fail to balance global exploration and local exploitation, occasionally becoming trapped in suboptimal regions on highly multimodal problems. The Groupers and Moray Eels (GME) algorithm, inspired by the associative hunting strategies of marine predators, provides a cooperative optimization framework. However, [...] Read more.
Metaheuristic algorithms can fail to balance global exploration and local exploitation, occasionally becoming trapped in suboptimal regions on highly multimodal problems. The Groupers and Moray Eels (GME) algorithm, inspired by the associative hunting strategies of marine predators, provides a cooperative optimization framework. However, the sequential interaction phases of GME can fail to maintain diverse topological coverage across heavily constrained landscapes. To address these limitations, we propose an enhanced variant, GPS-CC-GME. The approach improves the initial agent distribution by deploying a number-theoretic Good Point Set (GPS) generation protocol to establish a uniformly dispersed starting space. In addition, algorithmic stagnation is addressed through a dual-crossover search architecture. A horizontal crossover stage enforces information sharing among randomized agents to sustain global diversity, and a vertical crossover phase isolates specific dimensional vectors within individual agents for localized fine-tuning. We evaluated the proposed model on the CEC2017 benchmark suite, where it secured the highest overall ranking compared to the baseline GME and several standard metaheuristics. GPS-CC-GME was then applied to a high-dimensional optimization scenario for petroleum reservoir production. The algorithm yielded higher Net Present Value (NPV) metrics than the canonical framework. The results indicate that embedding deterministic initialization and bidirectional mutation operators into multipredator models can improve search outcomes in non-linear engineering tasks. Full article
(This article belongs to the Special Issue Bio-Inspired Computation and Its Applications)
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