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Biomimetics
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Advances in Biological and Bio-Inspired Algorithms
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Advances in Biological and Bio-Inspired Algorithms

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3526

Special Issue Editor

Dr. Yinggao Yue

E-Mail Website
Guest Editor
Oujiang College, Wenzhou University of Technology, Wenzhou, China
Interests: wireless communication; sensor networks; the Internet of Things

Special Issue Information

Dear Colleagues,

Optimization problems have always been a focus of research and have already existed in various real-world systems, such as fault diagnosis systems, energy management systems, and prediction systems. There are two main directions for the development of swarm intelligence optimization algorithms: one is to design new algorithms with a better performance; the other is to improve the update strategy of existing algorithms to achieve better optimization performance. Significant advancements have been made in biological and bio-inspired algorithms in the pursuit of increased flexibility and efficiency. Here, the ingenious systems seen in nature serve as the model for these algorithms. They are designed to mimic biological processes in order to enhance complex functions. Ongoing research and innovation have led to considerable improvements in their computational models and efficiency, with applications in a wide range of industries that are revolutionizing the way problems are resolved and systems are improved.

By improving the structural architecture of buildings, engineers may reduce material consumption while maintaining stability. They can dynamically alter data transmission channels in network routing to lower latency. In the field of energy management, they efficiently allocate electrical resources to balance supply and demand. These applications demonstrate how beneficial biological and bio-inspired algorithms are in a variety of fields.

Academic and industry research subjects include the development of algorithms in areas such as evolutionary computation inspired by natural selection, swarm intelligence based on social insect behavior, and neural network algorithms inspired by the structure of the human brain. The goal is to research and expand their potential applications. We look forward to your contributions.

Dr. Yinggao Yue
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • biological algorithms
  • bio-inspired algorithms
  • optimization
  • optimal design
  • computational efficiency
  • nature emulation
  • problem-solving
  • path optimization problem
  • dynamic adaptation
  • energy optimization
  • power system optimization
  • engineering optimization problem

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Published Papers (7 papers)

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Research

37 pages, 17246 KiB  
Article
A Multi-Strategy Improved Red-Billed Blue Magpie Optimizer for Global Optimization
by Mingjun Ye, Xiong Wang, Zihao Guo, Bin Hu and Li Wang
Biomimetics 2025, 10(9), 557; https://doi.org/10.3390/biomimetics10090557 - 22 Aug 2025
Abstract
To enhance the convergence efficiency and solution precision of the Red-billed Blue Magpie Optimizer (RBMO), this study proposes a Multi-Strategy Enhanced Red-billed Blue Magpie Optimizer (MRBMO). The principal methodological innovations encompass three aspects: (1) Development of a novel dynamic boundary constraint handling mechanism [...] Read more.
To enhance the convergence efficiency and solution precision of the Red-billed Blue Magpie Optimizer (RBMO), this study proposes a Multi-Strategy Enhanced Red-billed Blue Magpie Optimizer (MRBMO). The principal methodological innovations encompass three aspects: (1) Development of a novel dynamic boundary constraint handling mechanism that strengthens algorithmic exploration capabilities through adaptive regression strategy adjustment for boundary-transgressing particles; (2) Incorporation of an elite guidance strategy during the predation phase, establishing a guided search framework that integrates historical individual optimal information while employing a Lévy Flight strategy to modulate search step sizes, thereby achieving effective balance between global exploration and local exploitation capabilities; (3) Comprehensive experimental evaluations conducted on the CEC2017 and CEC2022 benchmark test suites demonstrate that MRBMO significantly outperforms classical enhanced algorithms and exhibits competitive performance against state-of-the-art optimizers across 41 standardized test functions. The practical efficacy of the algorithm is further validated through successful applications to four classical engineering design problems, confirming its robust problem-solving capabilities. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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23 pages, 5330 KiB  
Article
Explainable Reinforcement Learning for the Initial Design Optimization of Compressors Inspired by the Black-Winged Kite
by Mingming Zhang, Zhuang Miao, Xi Nan, Ning Ma and Ruoyang Liu
Biomimetics 2025, 10(8), 497; https://doi.org/10.3390/biomimetics10080497 - 29 Jul 2025
Viewed by 502
Abstract
Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach [...] Read more.
Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach that incorporates deep reinforcement learning and decision tree distillation to enhance both the optimization capability and explainability. First, a pre-selection platform for the initial design scheme of the compressors is constructed based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The optimization space is significantly enlarged by expanding the co-design of 25 key variables (e.g., the inlet airflow angle, the reaction, the load coefficient, etc.). Then, the initial design of six-stage axial compressors is successfully completed, with the axial efficiency increasing to 84.65% at the design speed and the surge margin extending to 10.75%. The design scheme is closer to the actual needs of engineering. Secondly, Shapley Additive Explanations (SHAP) analysis is utilized to reveal the influence of the mechanism of the key design parameters on the performance of the compressors in order to enhance the model explainability. Finally, the decision tree inspired by the black-winged kite (BKA) algorithm takes the interpretable design rules and transforms the data-driven intelligent optimization into explicit engineering experience. Through experimental validation, this method significantly improves the transparency of the design process while maintaining the high performance of the DDPG algorithm. The extracted design rules not only have clear physical meanings but also can effectively guide the initial design of the compressors, providing a new idea with both optimization capability and explainability for its intelligent design. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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22 pages, 3027 KiB  
Article
An Enhanced Grasshopper Optimization Algorithm with Outpost and Multi-Population Mechanisms for Dolomite Lithology Prediction
by Xinya Yu and Parhat Zunu
Biomimetics 2025, 10(8), 494; https://doi.org/10.3390/biomimetics10080494 - 25 Jul 2025
Viewed by 398
Abstract
The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost [...] Read more.
The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost Multi-population GOA (OMGOA). OMGOA integrates two novel mechanisms: the Outpost mechanism, which enhances local exploitation by guiding agents towards high-potential regions, and the multi-population enhanced mechanism, which promotes global exploration and maintains population diversity through parallel evolution and controlled information exchange. Comprehensive experiments were conducted to evaluate the effectiveness of OMGOA. Ablation studies were performed to assess the individual contributions of each mechanism, while multi-dimensional testing was used to verify robustness and scalability. Comparative experiments show that OMGOA has better optimization performance compared to other similar algorithms. In addition, OMGOA was successfully applied to a real-world engineering problem—lithology prediction from petrophysical logs—where it achieved competitive classification performance. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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15 pages, 2578 KiB  
Article
Multi-Feature Facial Complexion Classification Algorithms Based on CNN
by Xiyuan Cao, Delong Zhang, Chunyang Jin, Zhidong Zhang and Chenyang Xue
Biomimetics 2025, 10(6), 402; https://doi.org/10.3390/biomimetics10060402 - 13 Jun 2025
Viewed by 458
Abstract
Variations in facial complexion serve as a telltale sign of underlying health conditions. Precisely categorizing facial complexions poses a significant challenge due to the subtle distinctions in facial features. Three multi-feature facial complexion classification algorithms leveraging convolutional neural networks (CNNs) are proposed. They [...] Read more.
Variations in facial complexion serve as a telltale sign of underlying health conditions. Precisely categorizing facial complexions poses a significant challenge due to the subtle distinctions in facial features. Three multi-feature facial complexion classification algorithms leveraging convolutional neural networks (CNNs) are proposed. They fuse, splice, or independently train the features extracted from distinct facial regions of interest (ROI), respectively. Innovative frameworks of the three algorithms can more effectively exploit facial features, improving the utilization rate of feature information and classification performance. We trained and validated the three algorithms on the dataset consisting of 721 facial images that we had collected and preprocessed. The comprehensive evaluation reveals that multi-feature fusion and splicing classification algorithms achieve accuracies of 95.98% and 93.76%, respectively. The optimal approach combining multi-feature CNN with machine learning algorithms attains a remarkable accuracy of 97.78%. Additionally, these experiments proved that the multidomain combination was crucial, and the arrangement of ROI features, including the nose, forehead, philtrum, and right and left cheek, was the optimal choice for classification. Furthermore, we employed the EfficientNet model for training on the face image as a whole, which achieves a classification accuracy of 89.37%. The difference in accuracy underscores the superiority and efficacy of multi-feature classification algorithms. The employment of multi-feature fusion algorithms in facial complexion classification holds substantial advantages, ushering in fresh research directions in the field of facial complexion classification and deep learning. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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18 pages, 5409 KiB  
Article
Research on Motion Transfer Method from Human Arm to Bionic Robot Arm Based on PSO-RF Algorithm
by Yuanyuan Zheng, Hanqi Zhang, Gang Zheng, Yuanjian Hong, Zhonghua Wei and Peng Sun
Biomimetics 2025, 10(6), 392; https://doi.org/10.3390/biomimetics10060392 - 11 Jun 2025
Viewed by 542
Abstract
Although existing motion transfer methods for bionic robot arms are based on kinematic equivalence or simplified dynamic models, they frequently fail to tackle dynamic compliance and real-time adaptability in complex human-like motions. To address this shortcoming, this study presents a motion transfer method [...] Read more.
Although existing motion transfer methods for bionic robot arms are based on kinematic equivalence or simplified dynamic models, they frequently fail to tackle dynamic compliance and real-time adaptability in complex human-like motions. To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. Initially, a high-precision optical motion capture (Mocap) system was utilized to record human arm trajectories, and Kalman filtering and a Rauch–Tung–Striebel (RTS) smoother were applied to reduce noise and phase lag. Subsequently, the joint angles of the human arm were computed through geometric vector analysis. Although geometric vector analysis offers an initial estimation of joint angles, its deterministic framework is subject to error accumulation caused by the occlusion of reflective markers and kinematic singularities. To surmount this limitation, this study designed five action sequences for the establishment of the training database for the PSO-RF model to predict joint angles when performing different actions. Ultimately, an experimental platform was built to validate the motion transfer method, and the experimental verification showed that the system attained high prediction accuracy (R2 = 0.932 for the elbow joint angle) and real-time performance with a latency of 0.1097 s. This paper promotes compliant human–robot interaction by dealing with joint-level dynamic transfer challenges, presenting a framework for applications in intelligent manufacturing and rehabilitation robotics. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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34 pages, 3234 KiB  
Article
Improved Zebra Optimization Algorithm with Multi Strategy Fusion and Its Application in Robot Path Planning
by Zhengzong Wang, Xiantao Ye, Guolin Jiang and Yiru Yi
Biomimetics 2025, 10(6), 354; https://doi.org/10.3390/biomimetics10060354 - 1 Jun 2025
Viewed by 651
Abstract
In order to overcome the inherent drawbacks of the baseline Zebra Optimization Algorithm (ZOA) approach, such as its propensity for premature convergence and local optima trapping, this work creates a Multi-Strategy Enhanced Zebra Optimization Algorithm (MZOA). Three strategic changes are incorporated into the [...] Read more.
In order to overcome the inherent drawbacks of the baseline Zebra Optimization Algorithm (ZOA) approach, such as its propensity for premature convergence and local optima trapping, this work creates a Multi-Strategy Enhanced Zebra Optimization Algorithm (MZOA). Three strategic changes are incorporated into the improved framework: triangular walk operators to balance localized exploitation and global exploration across optimization phases; Levy flight mechanisms to strengthen solution space traversal capabilities; and lens imaging inversion learning to improve population diversity and avoid local convergence stagnation. The enhanced solution accuracy of the MZOA over modern metaheuristics is empirically validated using the CEC2005 and CEC2017 benchmark suites. The proposed MZOA’s performance improved by 15.8% compared to the basic ZOA The algorithm’s practical effectiveness across a range of environmental difficulties is confirmed by extensive assessment in engineering optimization and robotic route planning scenarios. It routinely achieves optimal solutions in both simple and complicated setups. In robot path planning, the proposed MZOA reduces the movement path by 8.7% compared to the basic ZOA. These comprehensive evaluations establish the MZOA as a robust computational algorithm for complex optimization challenges, demonstrating enhanced convergence characteristics and operational reliability in synthetic and real-world applications. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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26 pages, 5926 KiB  
Article
Path Optimization Strategy for Unmanned Aerial Vehicles Based on Improved Black Winged Kite Optimization Algorithm
by Shuxin Wang, Bingruo Xu, Yejun Zheng, Yinggao Yue and Mengji Xiong
Biomimetics 2025, 10(5), 310; https://doi.org/10.3390/biomimetics10050310 - 11 May 2025
Cited by 1 | Viewed by 707
Abstract
The Black-winged Kite Optimization Algorithm (BKA) is likely to experience a sluggish convergence rate when confronted with the optimization of complex multimodal functions. The fundamental algorithm has a tendency to get stuck in local optima, thus rendering it arduous to identify the global [...] Read more.
The Black-winged Kite Optimization Algorithm (BKA) is likely to experience a sluggish convergence rate when confronted with the optimization of complex multimodal functions. The fundamental algorithm has a tendency to get stuck in local optima, thus rendering it arduous to identify the global optimal solution. When dealing with large-scale data or high-dimensional optimization challenges, the BKA algorithm entails significant computational expenses, which might lead to excessive memory usage or prolonged running durations. In order to enhance the BKA and tackle these problems, a revised Black-winged Kite Optimization Algorithm (TGBKA) that incorporates the Tent chaos mapping and Gaussian mutation strategies is put forward. The algorithm is simulated and analyzed alongside other swarm intelligence algorithms by utilizing the CEC2017 test function set. The optimization outcomes of the test functions and the function convergence curves indicate that the TGBKA demonstrates superior optimization precision, a quicker convergence speed, as well as robust anti-interference and environmental adaptability. It is also contrasted with numerous similar algorithms via simulation experiments in various scene models for Unmanned Aerial Vehicle (UAV) path planning. In comparison to other algorithms, the TGBKA produces a shorter flight route, a higher convergence speed, and stronger adaptability to complex environments. It is capable of efficiently addressing UAV path planning issues and improving the UAV’s path planning abilities. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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