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Biomimetics
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Nature-Inspired Metaheuristic Optimization Algorithms 2025
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Nature-Inspired Metaheuristic Optimization Algorithms 2025

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

Deadline for manuscript submissions: closed (20 July 2025) | Viewed by 10280

Special Issue Editor

Dr. Ameer Hamza Khan

E-Mail Website
Guest Editor
Smart City Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong
Interests: SLAM; control systems; robotics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Developing computationally efficient algorithms has been at the forefront of research and development in recent years. With the advent of big data, deep learning, and artificial intelligence (AI), prioritizing computationally efficient software and hardware systems has become a primary design objective. Optimization algorithms are an integral part of all real-world systems. Although traditional gradient-based optimization methods have been rigorously studied over the years, they put several analytical constraints on the objective function, e.g., continuity, differentiability, and convexity. Additionally, an analytical model of the system should be a priori, which can be difficult to formulate for several real-world systems. These algorithms also do not apply to discontinuous and discrete systems. Even if the analytical model is known to be continuous and differentiable, the computational requirement of gradient and Hessians makes them expensive to implement.

Metaheuristic optimization algorithms inspired by natural processes and the behavior of biological organisms present themselves as an effective alternative to the traditional gradient-based algorithms. They have also been extensively explored in recent years and are rapidly finding applications in real-world systems. These algorithms are formulated on the principles of biomimetics, i.e., mimicking the behavior of biological systems to solve an optimization problem. The behavior of biological organisms has been optimized over millions of years through the process of natural selection. Every species has developed traits (mostly instinctual) necessary for survival in nature. Modeling this behavior as a mathematical algorithm presents great potential in regard to developing computationally efficient optimization algorithms. For example, evolutionary algorithms (EAs) and genetic algorithms (GAs) are inspired by the process of genetic mutations and the survival of the fittest. Similarly, other algorithms, like the particle swarm optimizer (PSO), gray wolf optimizer (GWO), and beetle antennae search (BAS) are inspired by the behavior of birds and insects and their ability to accomplish a task in a decentralized manner by just following their basic biological instincts and not needing any elaborate planning and centralized communication.

We are organizing this Special Issue to gather the latest research related to nature-inspired metaheuristic optimization algorithms and their applications. The application of a bio-inspired metaheuristic algorithm in real-world systems will draw greater research attention to biomimetics.

Dr. Ameer Hamza Khan
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

  • bio-inspired algorithms
  • metaheuristic optimization
  • gradient-free algorithms
  • evolutionary algorithms (EAs)
  • computational efficiency
  • swarm intelligence

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Related Special Issue

Published Papers (12 papers)

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49 pages, 24339 KiB  
Article
An Enhanced Slime Mould Algorithm Based on Best–Worst Management for Numerical Optimization Problems
by Tongzheng Li, Hongchi Meng, Dong Wang, Bin Fu, Yuanyuan Shao and Zhenzhong Liu
Biomimetics 2025, 10(8), 504; https://doi.org/10.3390/biomimetics10080504 - 1 Aug 2025
Viewed by 326
Abstract
The Slime Mould Algorithm (SMA) is a widely used swarm intelligence algorithm. Encouraged by the theory of no free lunch and the inherent shortcomings of the SMA, this work proposes a new variant of the SMA, called the BWSMA, in which three improvement [...] Read more.
The Slime Mould Algorithm (SMA) is a widely used swarm intelligence algorithm. Encouraged by the theory of no free lunch and the inherent shortcomings of the SMA, this work proposes a new variant of the SMA, called the BWSMA, in which three improvement mechanisms are integrated. The adaptive greedy mechanism is used to accelerate the convergence of the algorithm and avoid ineffective updates. The best–worst management strategy improves the quality of the population and increases its search capability. The stagnant replacement mechanism prevents the algorithm from falling into a local optimum by replacing stalled individuals. In order to verify the effectiveness of the proposed method, this paper conducts a full range of experiments on the CEC2018 test suite and the CEC2022 test suite and compares BWSMA with three derived algorithms, eight SMA variants, and eight other improved algorithms. The experimental results are analyzed using the Wilcoxon rank-sum test, the Friedman test, and the Nemenyi test. The results indicate that the BWSMA significantly outperforms these compared algorithms. In the comparison with the SMA variants, the BWSMA obtained average rankings of 1.414, 1.138, 1.069, and 1.414. In comparison with other improved algorithms, the BWSMA obtained average rankings of 2.583 and 1.833. Finally, the applicability of the BWSMA is further validated through two structural optimization problems. In conclusion, the proposed BWSMA is a promising algorithm with excellent search accuracy and robustness. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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47 pages, 10439 KiB  
Article
Adaptive Nonlinear Bernstein-Guided Parrot Optimizer for Mural Image Segmentation
by Jianfeng Wang, Jiawei Fan, Xiaoyan Zhang and Bao Qian
Biomimetics 2025, 10(8), 482; https://doi.org/10.3390/biomimetics10080482 - 22 Jul 2025
Viewed by 237
Abstract
During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods [...] Read more.
During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods suffer from suboptimal segmentation quality. To improve mural image segmentation, this study proposes an efficient mural image segmentation method termed Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating an adaptive learning strategy, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO). In ANBPO, First, to address PO’s limited global exploration capability, the adaptive learning strategy is introduced. By considering individual information disparities and learning behaviors, this strategy effectively enhances the algorithm’s global exploration, enabling a thorough search of the solution space. Second, to mitigate the imbalance between PO’s global exploration and local exploitation phases, the nonlinear factor is proposed. Leveraging its adaptability and nonlinear curve characteristics, this factor improves the algorithm’s ability to escape local optimal segmentation thresholds. Finally, to overcome PO’s inadequate local exploitation capability, the third-order Bernstein-guided strategy is introduced. By incorporating the weighted properties of third-order Bernstein polynomials, this strategy comprehensively evaluates individuals with diverse characteristics, thereby enhancing the precision of mural image segmentation. ANBPO was applied to segment twelve mural images. The results demonstrate that, compared to competing algorithms, ANBPO achieves a 91.6% win rate in fitness function values while outperforming them by 67.6%, 69.4%, and 69.7% in PSNR, SSIM, and FSIM metrics, respectively. These results confirm that the ANBPO algorithm can effectively segment mural images while preserving the original feature information. Thus, it can be regarded as an efficient mural image segmentation algorithm. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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27 pages, 5788 KiB  
Article
A Novel Artificial Eagle-Inspired Optimization Algorithm for Trade Hub Location and Allocation Method
by Shuhan Hu, Gang Hu, Bo Du and Abdelazim G. Hussien
Biomimetics 2025, 10(8), 481; https://doi.org/10.3390/biomimetics10080481 - 22 Jul 2025
Viewed by 303
Abstract
Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total [...] Read more.
Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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37 pages, 33539 KiB  
Article
Domain-Separated Quantum Neural Network for Truss Structural Analysis with Mechanics-Informed Constraints
by Hyeonju Ha, Sudeok Shon and Seungjae Lee
Biomimetics 2025, 10(6), 407; https://doi.org/10.3390/biomimetics10060407 - 16 Jun 2025
Viewed by 658
Abstract
This study proposes an index-based quantum neural network (QNN) model, built upon a variational quantum circuit (VQC), as a surrogate framework for the static analysis of truss structures. Unlike coordinate-based models, the proposed QNN uses discrete member and node indices as inputs, and [...] Read more.
This study proposes an index-based quantum neural network (QNN) model, built upon a variational quantum circuit (VQC), as a surrogate framework for the static analysis of truss structures. Unlike coordinate-based models, the proposed QNN uses discrete member and node indices as inputs, and it adopts a separate-domain strategy that partitions the structure for parallel training. This architecture reflects the way nature organizes and optimizes complex systems, thereby enhancing both flexibility and scalability. Independent quantum circuits are assigned to each separate domain, and a mechanics-informed loss function based on the force method is formulated within a Lagrangian dual framework to embed physical constraints directly into the training process. As a result, the model achieves high prediction accuracy and fast convergence, even under complex structural conditions with relatively few parameters. Numerical experiments on 2D and 3D truss structures show that the QNN reduces the number of parameters by up to 64% compared to conventional neural networks, while achieving higher accuracy. Even within the same QNN architecture, the separate-domain approach outperforms the single-domain model with a 6.25% reduction in parameters. The proposed index-based QNN model has demonstrated practical applicability for structural analysis and shows strong potential as a quantum-based numerical analysis tool for future applications in building structure optimization and broader engineering domains. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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41 pages, 12098 KiB  
Article
An Enhanced Human Evolutionary Optimization Algorithm for Global Optimization and Multi-Threshold Image Segmentation
by Liang Xiang, Xiajie Zhao, Jianfeng Wang and Bin Wang
Biomimetics 2025, 10(5), 282; https://doi.org/10.3390/biomimetics10050282 - 1 May 2025
Viewed by 589
Abstract
Thresholding image segmentation aims to divide an image into a number of regions with different feature attributes in order to facilitate the extraction of image features in the context of image detection and pattern recognition. However, existing threshold image-segmentation methods suffer from the [...] Read more.
Thresholding image segmentation aims to divide an image into a number of regions with different feature attributes in order to facilitate the extraction of image features in the context of image detection and pattern recognition. However, existing threshold image-segmentation methods suffer from the problem of easily falling into locally optimal thresholds, resulting in poor image segmentation. In order to improve the image-segmentation performance, this study proposes an enhanced Human Evolutionary Optimization Algorithm (HEOA), known as CLNBHEOA, which incorporates Otsu’s method as an objective function to significantly improve the image-segmentation performance. In the CLNBHEOA, firstly, population diversity is enhanced using the Chebyshev–Tent chaotic mapping refraction opposites-based learning strategy. Secondly, an adaptive learning strategy is proposed which combines differential learning and adaptive factors to improve the ability of the algorithm to jump out of the locally optimum threshold. In addition, a nonlinear control factor is proposed to better balance the global exploration phase and the local exploitation phase of the algorithm. Finally, a three-point guidance strategy based on Bernstein polynomials is proposed which enhances the local exploitation ability of the algorithm and effectively improves the efficiency of optimal threshold search. Subsequently, the optimization performance of the CLNBHEOA was evaluated on the CEC2017 benchmark functions. Experiments demonstrated that the CLNBHEOA outperformed the comparison algorithms by over 90%, exhibiting higher optimization performance and search efficiency. Finally, the CLNBHEOA was applied to solve six multi-threshold image-segmentation problems. The experimental results indicated that the CLNBHEOA achieved a winning rate of over 95% in terms of fitness function value, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM), suggesting that it can be considered a promising approach for multi-threshold image segmentation. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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20 pages, 4879 KiB  
Article
A Comparison of Binary and Integer Encodings in Genetic Algorithms for the Maximum k-Coverage Problem with Various Genetic Operators
by Yoon Choi, Jingeun Kim and Yourim Yoon
Biomimetics 2025, 10(5), 274; https://doi.org/10.3390/biomimetics10050274 - 28 Apr 2025
Viewed by 544
Abstract
The maximum k-coverage problem (MKCP) is a problem of finding a solution that includes the maximum number of covered rows by selecting k columns from an m ×n matrix of 0s and 1s. This is an NP-hard problem that is [...] Read more.
The maximum k-coverage problem (MKCP) is a problem of finding a solution that includes the maximum number of covered rows by selecting k columns from an m ×n matrix of 0s and 1s. This is an NP-hard problem that is difficult to solve in a realistic time; therefore, it cannot be solved with a general deterministic algorithm. In this study, genetic algorithms (GAs), an evolutionary arithmetic technique, were used to solve the MKCP. Genetic algorithms (GAs) are meta-heuristic algorithms that create an initial solution group, select two parent solutions from the solution group, apply crossover and repair operations, and replace the generated offspring with the previous parent solution to move to the next generation. Here, to solve the MKCP with binary and integer encoding, genetic algorithms were designed with various crossover and repair operators, and the results of the proposed algorithms were demonstrated using benchmark data from the OR-library. The performances of the GAs with various crossover and repair operators were also compared for each encoding type through experiments. In binary encoding, the combination of uniform crossover and random repair improved the average objective value by up to 3.24% compared to one-point crossover and random repair across the tested instances. The conservative repair method was not suitable for binary encoding compared to the random repair method. In contrast, in integer encoding, the combination of uniform crossover and conservative repair achieved up to 4.47% better average performance than one-point crossover and conservative repair. The conservative repair method was less suitable with one-point crossover operators than the random repair method, but, with uniform crossover, was better. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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36 pages, 2866 KiB  
Article
Optimizing the Design of TES Tanks for Thermal Energy Storage Applications Through an Integrated Biomimetic-Genetic Algorithm Approach
by Nadiya Mehraj, Carles Mateu, Gabriel Zsembinszki and Luisa F. Cabeza
Biomimetics 2025, 10(4), 197; https://doi.org/10.3390/biomimetics10040197 - 24 Mar 2025
Cited by 1 | Viewed by 991
Abstract
Building upon an experimentally validated bio-inspired thermal energy storage (TES) tank design, this study introduced a novel computational framework that integrated genetic algorithms (GA) with biomimetic principles to systematically generate TES tank geometries. Inspired by natural thermal distribution patterns found in vascular networks, [...] Read more.
Building upon an experimentally validated bio-inspired thermal energy storage (TES) tank design, this study introduced a novel computational framework that integrated genetic algorithms (GA) with biomimetic principles to systematically generate TES tank geometries. Inspired by natural thermal distribution patterns found in vascular networks, the AI-driven methodology explored 13 geometric parameters, focusing on branching structures and spatial distribution, and resulted in computationally generated designs with a 29% increase in heat transfer surface area while maintaining manufacturability constraints within a fixed tank diameter of 150 mm and height of 155 mm. Unlike previous biomimetic TES studies that relied on predefined geometric configurations, this approach developed AI-driven bio-inspired structures within experimentally validated dimensional constraints, ensuring geometric relevance while allowing for broader structural exploration. The resulting designs exhibited key characteristics of high-efficiency bio-inspired configurations while providing a systematic, scalable methodology for TES tank architecture. This study represented the first step in integrating AI-driven biomimicry into TES tank design, establishing a structured framework for generating high-performance, manufacturable configurations. While the current work focused on computational design, future research will emphasize experimental validation and real-world implementation to confirm the practical thermal and structural benefits of these AI-generated bio-inspired designs. By bridging the gap between computational intelligence and nature-inspired engineering, this research provided a scalable pathway for developing more efficient, manufacturable, and sustainable TES solutions for energy storage applications. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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31 pages, 5646 KiB  
Article
Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning
by Yidao Ji, Qiqi Liu, Cheng Zhou, Zhiji Han and Wei Wu
Biomimetics 2025, 10(3), 180; https://doi.org/10.3390/biomimetics10030180 - 14 Mar 2025
Cited by 1 | Viewed by 919
Abstract
Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard [...] Read more.
Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle Swarm Optimization algorithm. Specifically, competitive and supportive behaviors are mathematically modeled to enhance particle learning strategies and improve global search capabilities in the mid-optimization phase. To mitigate the risk of convergence to local optima in later stages, a mutation mechanism is introduced to enhance population diversity and overall accuracy. To address the challenges of urban drone path planning, this paper proposes an innovative method that combines a path segmentation and prioritized update algorithm with a cubic B-spline curve algorithm. This method enhances both path optimality and smoothness, ensuring safe and efficient navigation in complex urban settings. Comparative simulations demonstrate the effectiveness of the proposed approach, yielding smoother trajectories and improved real-time performance. Additionally, the method significantly reduces energy consumption and operation time. Overall, this research advances drone path planning technology and broadens its applicability in diverse urban environments. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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24 pages, 2820 KiB  
Article
An Enhanced Misinformation Detection Model Based on an Improved Beluga Whale Optimization Algorithm and Cross-Modal Feature Fusion
by Guangyu Mu, Xiaoqing Ju, Hongduo Yan, Jiaxue Li, He Gao and Xiurong Li
Biomimetics 2025, 10(3), 128; https://doi.org/10.3390/biomimetics10030128 - 20 Feb 2025
Viewed by 828
Abstract
The proliferation of multimodal misinformation on social media has become a critical concern. Although detection methods have advanced, feature representation and cross-modal semantic alignment challenges continue to hinder the effective use of multimodal data. Therefore, this paper proposes an IBWO-CASC detection model that [...] Read more.
The proliferation of multimodal misinformation on social media has become a critical concern. Although detection methods have advanced, feature representation and cross-modal semantic alignment challenges continue to hinder the effective use of multimodal data. Therefore, this paper proposes an IBWO-CASC detection model that integrates an improved Beluga Whale Optimization algorithm with cross-modal attention feature fusion. Firstly, the Beluga Whale Optimization algorithm is enhanced by combining adaptive search mechanisms with batch parallel strategies in the feature space. Secondly, a feature alignment method is designed based on supervised contrastive learning to establish semantic consistency. Then, the model incorporates a Cross-modal Attention Promotion mechanism and global–local interaction learning pattern. Finally, a multi-task learning framework is built based on classification and contrastive objectives. The empirical analysis shows that the proposed IBWO-CASC model achieves a detection accuracy of 97.41% on our self-constructed multimodal misinformation dataset. Compared with the average accuracy of the existing six baseline models, the accuracy of this model is improved by 4.09%. Additionally, it demonstrates enhanced robustness in handling complex multimodal scenarios. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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42 pages, 26326 KiB  
Article
A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems
by Wuke Li, Xiong Yang, Yuchen Yin and Qian Wang
Biomimetics 2025, 10(1), 14; https://doi.org/10.3390/biomimetics10010014 - 31 Dec 2024
Cited by 3 | Viewed by 1489
Abstract
The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of [...] Read more.
The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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Review

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22 pages, 7229 KiB  
Review
Evolution and Trends of the Exploration–Exploitation Balance in Bio-Inspired Optimization Algorithms: A Bibliometric Analysis of Metaheuristics
by Yoslandy Lazo, Broderick Crawford, Felipe Cisternas-Caneo, José Barrera-Garcia, Ricardo Soto and Giovanni Giachetti
Biomimetics 2025, 10(8), 517; https://doi.org/10.3390/biomimetics10080517 - 7 Aug 2025
Abstract
The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study [...] Read more.
The balance between exploration and exploitation is a fundamental element in the design and performance of bio-inspired optimization algorithms. However, to date, its conceptual evolution and its treatment in the scientific literature have not been systematically characterized from a bibliometric approach. This study performs an exhaustive analysis of the scientific production on the balance between exploration and exploitation using records extracted from the Web of Science (WoS) database. The processing and analysis of the data were carried out through the combined use of Bibliometrix (R package) and VOSviewer, tools that made it possible to quantify productivity, map collaborative networks, and visualize emerging thematic trends. The results show a sustained growth in the volume of publications over the last decade, as well as the consolidation of academic collaboration networks and the emergence of new thematic lines in the field. In particular, metaheuristic algorithms have demonstrated a significant and growing impact, constituting a fundamental pillar in the advancement and methodological diversification of the exploration–exploitation balance. This work provides a quantitative framework and a structured view of the evolution of research, identifies the main actors and trends, and raises opportunities for future lines of research in the field of optimization using metaheuristics, the most prominent instantiation of bio-inspired optimization algorithms. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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Other

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46 pages, 1999 KiB  
Systematic Review
Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review
by Álex Paz, Broderick Crawford, Eric Monfroy, José Barrera-García, Álvaro Peña Fritz, Ricardo Soto, Felipe Cisternas-Caneo and Andrés Yáñez
Biomimetics 2025, 10(5), 326; https://doi.org/10.3390/biomimetics10050326 - 17 May 2025
Viewed by 746
Abstract
Credit risk assessment plays a critical role in financial risk management, focusing on predicting borrower default to minimize losses and ensure compliance. This study systematically reviews 23 empirical articles published between 2019 and 2023, highlighting the integration of machine learning and optimization techniques, [...] Read more.
Credit risk assessment plays a critical role in financial risk management, focusing on predicting borrower default to minimize losses and ensure compliance. This study systematically reviews 23 empirical articles published between 2019 and 2023, highlighting the integration of machine learning and optimization techniques, particularly bio-inspired metaheuristics, for feature selection in individual credit risk assessment. These nature-inspired algorithms, derived from biological and ecological processes, align with bio-inspired principles by mimicking natural intelligence to solve complex problems in high-dimensional feature spaces. Unlike prior reviews that adopt broader scopes combining corporate, sovereign, and individual contexts, this work focuses exclusively on methodological strategies for individual credit risk. It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. To strengthen transparency and comparability, this review also synthesizes classification performance metrics—such as accuracy, AUC, F1-score, and recall—reported across benchmark datasets. Although no unified experimental comparison was conducted due to heterogeneity in study protocols, this structured summary reveals consistent trends in algorithm effectiveness and evaluation practices. The review concludes with practical recommendations and outlines future research directions to improve fairness, scalability, and real-time application in credit risk modeling. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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