Submit to Special Issue Submit Abstract to Special Issue Review for Biomimetics Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advanced Nature-Inspired Optimization Algorithms

Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A Special Issue of Biomimetics (ISSN 2313-7673) belonging to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 7511

Share This Special Issue

Editors

Dr. Spyridon D. Mourtas

E-Mail Website
Guest Editor

Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, 10559 Athens, Greece
Interests: applied linear algebra; mathematical finance; mathematical optimization; neural networks; intelligent optimization

Prof. Dr. Vasilios N. Katsikis

E-Mail Website
Guest Editor

Department of Economics, Division of Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
Interests: artificial intelligence; computational optimization; intelligent optimization; computational finance; mathematical finance
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shuai Li

E-Mail Website
Guest Editor

Faculty of Information Technology and Electrical Engineering, University of Oulu, 90100 Oulu, Finland
Interests: neural networks; nonlinear optimization; optimal control; robotic planning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xinwei Cao

E-Mail Website
Guest Editor

School of Business, Jiangnan University, Wuxi 214122, China
Interests: portfolio optimization; big data; fintech management and decision making; fraud detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Nature-inspired optimization algorithms have become a cornerstone of modern computational intelligence, offering robust, flexible, and efficient solutions to complex, nonlinear, and high-dimensional problems. Inspired by biological evolution, collective intelligence, and natural adaptive processes, these methods have demonstrated remarkable success across a wide range of scientific, engineering, and socio-economic applications.

This Special Issue, “Advanced Nature-Inspired Optimization Algorithms”, aims to provide a comprehensive forum for cutting-edge research on the development, analysis, and application of biologically inspired optimization techniques. Emphasis is placed on both theoretical advances and real-world applications, including hybrid and intelligent optimization frameworks that combine evolutionary algorithms, swarm intelligence, neural networks, fuzzy systems, and other bio-inspired paradigms.

The Special Issue particularly welcomes contributions addressing complex optimization problems arising in engineering, artificial intelligence, data science, economics, finance, management, and decision-making systems. Novel algorithmic designs, performance analysis, benchmarking, and application-driven studies are all within scope, provided that a clear bio-inspired rationale is demonstrated.

By bringing together researchers from diverse disciplines, this Special Issue aims to highlight recent advances, emerging trends, and future challenges in nature-inspired optimization, fostering cross-disciplinary innovation within the broader biomimetics community.

Dr. Spyridon D. Mourtas
Prof. Dr. Vasilios N. Katsikis
Prof. Dr. Shuai Li
Prof. Dr. Xinwei Cao
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

nature-inspired optimization
biological optimization
bio-inspired algorithms
evolutionary algorithms
awarm intelligence
intelligent optimization
hybrid optimization methods
fuzzy systems
neural networks
neuro-fuzzy systems
metaheuristic algorithms
computational intelligence
machine learning
optimization in engineering
optimization in economics and finance
decision-making systems
multi-objective optimization
complex systems optimization

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (12 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

27 pages, 3397 KB

Open AccessArticle

IRCMO: Biologically Inspired Dual-Population Coevolution via Immune Tolerance and Homeostatic Resource Allocation for Constrained Multi-Objective Optimization

by Xiaoguo Chen, Yongchao Li, Xingsen Li and Yue Yang

Biomimetics 2026, 11(9), 666; https://doi.org/10.3390/biomimetics11090666 - 16 Sep 2026

Viewed by 76

Abstract

Constrained multiobjective optimization seeks Pareto optimal tradeoffs among conflicting objectives subject to prescribed constraints. Although the final solutions must be feasible, evolutionary variation can generate infeasible intermediate decision vectors during the search. One difficulty in this process is identifying mildly infeasible candidates that still provide valuable search directions, while another is distributing a fixed evaluation budget between cooperative populations whose contributions vary during evolution. To address these issues, this paper proposes IRCMO, a biologically inspired dual population constrained multiobjective evolutionary algorithm. The main population focuses on approximating the Pareto front formed by feasible solutions, while the auxiliary population preserves complementary search directions near constraint boundaries in the decision space. An Immune Tolerance and Niche Exclusion Selection strategy (ITNES) adaptively determines a tolerance boundary from the current feasibility status. It applies a squared response only to excess constraint violation and preserves sparse search directions in both the objective and decision spaces. This enables the auxiliary population to exploit mildly infeasible candidates without losing feasibility pressure, thereby improving convergence and front coverage under restrictive constraint structures. A Replicator Complementarity Homeostatic Resource Allocation strategy (RCHRA) evaluates offspring improvement and cross population complementarity. It assigns more offspring evaluations to the population making a stronger current contribution while maintaining a minimum resource share for both populations. This improves the utilization of the fixed evaluation budget and reduces persistent search imbalance between the two populations. Experiments on 47 benchmark problems and 12 real world CMOPs against seven representative algorithms show that IRCMO obtains the lowest overall average ranks for both IGD and HV. All pairwise Wilcoxon tests are significant at the 0.05 level. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

19 pages, 452 KB

Open AccessArticle

Enhanced Moss Growth Optimization with Benchmark Validation and a Wastewater Treatment Prediction Case Study

by Zongkun Li and Shanfa Tang

Biomimetics 2026, 11(9), 628; https://doi.org/10.3390/biomimetics11090628 - 3 Sep 2026

Viewed by 249

Abstract

Complex optimization tasks in data-driven prediction and engineering applications often involve nonlinear, multimodal, and ill-conditioned objective functions. This study proposes an Enhanced Moss Growth Optimization algorithm (EMGO), an improved variant of the baseline MGO framework, to enhance exploratory step-size control and local covariance exploitation. EMGO incorporates two key algorithmic augmentations: a budget-adaptive jump regulation mechanism that balances global dispersal and fine-grained refinement, and a shrinkage-regularized covariance-guided sampling operator with relative eigenvalue flooring to exploit correlation structures among elite individuals without rank deficiency. The proposed algorithm is evaluated on the CEC2017 benchmark suite across 50 and 100 dimensions with 29 test functions, 30 independent runs, and a budget of

3 \times 10^{5}

function evaluations per run, compared against ten state-of-the-art optimizers including CMA-ES, L-SHADE, SBO, and baseline MGO. Nonparametric Friedman ranking, Holm-adjusted Wilcoxon signed-rank tests, and runtime-matched analyses demonstrate that EMGO achieves highly competitive performance across high-dimensional landscapes. Furthermore, EMGO is applied to tune support vector regression (SVR) hyperparameters for effluent suspended solid (SS) prediction using the UCI Water Treatment Plant dataset under an expanding-window rolling-origin cross-validation scheme. EMGO-SVR achieves superior predictive accuracy (

RMSE = 5.58 \pm 0.64

MAE = 3.97 \pm 0.46

R^{2} = 0.889 \pm 0.028

), outperforming standard SVR, tree-based ensembles, and Bayesian optimization baselines. SHAP-based feature importance analysis confirms the physical and process consistency of the model predictions. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

34 pages, 4164 KB

Open AccessArticle

A Q-Learning-Based Hyper-Heuristic Genetic Algorithm for Optimizing Human–Robot Collaborative Assembly Lines

by Seçil Kulaç

Biomimetics 2026, 11(8), 600; https://doi.org/10.3390/biomimetics11080600 - 21 Aug 2026

Viewed by 322

Abstract

Human–robot collaborative assembly line balancing and scheduling constitutes an NP-hard combinatorial optimization problem involving the simultaneous optimization of task assignment, resource allocation, processing mode selection, station-level scheduling, and ergonomic constraints. This study proposes a Q-learning-based hyper-heuristic genetic algorithm (QLHH-GA) to solve the cost-oriented ergonomic mixed-model human–robot collaborative assembly line balancing and scheduling problem. The proposed approach integrates bio-inspired evolutionary mechanisms of population variation and selection with adaptive, Q-learning-guided low-level heuristic selection. The Q-learning layer uses performance feedback to adapt the search strategy to different solution states while maintaining solution feasibility. A mixed-integer linear programming (MILP) model is also developed to minimize the total operating cost, including station opening, labor, robot operation, and energy consumption costs, while enforcing station-level energy expenditure (EE) limits. Computational experiments conducted using benchmark instances of varying sizes and a literature-based industrial case study demonstrate that QLHH-GA produces solutions comparable to those obtained by the MILP model on small-scale instances and maintains strong solution quality on larger instances, for which exact optimization becomes computationally prohibitive. These findings demonstrate the scalability and effectiveness of reinforcement-learning-guided hyper-heuristic search for designing cost-efficient and ergonomically constrained human–robot collaborative assembly lines. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Graphical abstract

23 pages, 5128 KB

Open AccessArticle

An Improved Artificial Lemming Algorithm and Its Preliminary Application to NIR-Based Prediction of Dendrobium huoshanense Polysaccharides

by Yu Liu, Feilong Yu, Yaqi Yang, Xingyu Gao, Maosheng Fu, Chaochuan Jia and Zhengyu Liu

Biomimetics 2026, 11(8), 590; https://doi.org/10.3390/biomimetics11080590 - 18 Aug 2026

Viewed by 296

Abstract

Dendrobium polysaccharide is an important indicator for evaluating the quality of Dendrobium huoshanense. To improve the prediction accuracy of polysaccharide content, this study proposes an improved Artificial Lemming Algorithm (IALA) optimized BP neural network model. In IALA, a periodic mutation strategy and a fast hybrid opposition learning strategy (FHOBL) are introduced to enhance population diversity, improve global search ability, and avoid premature convergence. The proposed IALA was first evaluated on CEC2017 and CEC2020 benchmark functions. Experimental results show that IALA achieves better or competitive performance compared with seven other algorithms in terms of mean fitness, best fitness, and standard deviation. Statistical tests, including Wilcoxon rank-sum and Friedman tests, further verify the significant superiority and robustness of IALA. Then, IALA was used to optimize the initial weights and thresholds of BP neural networks for Dendrobium polysaccharide content prediction. The results show that IALA-BP achieves the best overall prediction performance, with an R² of 0.8731, RMSE of 2.1581, and MSE of 4.6683. Compared with standard BP and other optimized BP models, IALA-BP provides more accurate and stable prediction results. Therefore, the proposed IALA-BP model is effective for rapid prediction of Dendrobium polysaccharide content. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

36 pages, 4542 KB

Open AccessArticle

Experimental Characterization of Emergent Behavior in Bio-Inspired Swarm Intelligence Algorithms

by Yoslandy Lazo, Broderick Crawford, Gino Astorga, Felipe Cisternas-Caneo, José Barrera-Garcia, Giovanni Giachetti and Ricardo Soto

Biomimetics 2026, 11(8), 576; https://doi.org/10.3390/biomimetics11080576 - 12 Aug 2026

Viewed by 350

Abstract

Bio-inspired swarm metaheuristic algorithms constitute a widely used tool for solving complex optimization problems. However, the experimental characterization of their emergent behavior remains a methodological challenge. This study proposes an experimental framework for characterizing emergent behavior through swarm collective dynamics. The framework integrates complementary dynamic indicators and establishes relative diversity loss as a homogeneous criterion for defining equivalent comparison states across different search processes. The framework was evaluated using the Reptile Search Algorithm (RSA) and Draco Lizard Optimizer (DLO) as case studies, with Particle Swarm Optimization (PSO) serving as a reference algorithm. The results showed that swarm collective dynamics were associated with both the mathematical properties of the search landscape and the search mechanisms of each metaheuristic. Furthermore, relative diversity loss enabled the comparison of different metaheuristics within a common reference framework. In RSA, swarm reorganization occurred during the first iterations. DLO exhibited a more gradual evolution, whereas PSO showed an intermediate behavior between both dynamics. The proposed experimental framework provides a methodological basis for the experimental characterization of emergent behavior in swarm metaheuristics. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Graphical abstract

41 pages, 7681 KB

Open AccessArticle

An Improved Elk Herd Optimiser (IEHO)

by Yanjiao Wang, Fei Du and Li Chuai

Biomimetics 2026, 11(8), 527; https://doi.org/10.3390/biomimetics11080527 - 25 Jul 2026

Viewed by 290

Abstract

The Elk Herd Optimiser (EHO) is a novel metaheuristic algorithm inspired by the reproductive behaviour of elk herds. However, it suffers from insufficient convergence accuracy and population diversity. To address these issues, this study proposes an improved EHO (IEHO). A novel individual update strategy for the breeding phase is introduced to meet the requirements of convergence speed and diversity during evolution. A new population grouping strategy is also developed to achieve a dual balance between elite guidance and spatial distribution. Cauchy distribution sampling is used to generate learning weights, and population diversity is adopted to control the step size of movement, allowing real-time monitoring and supplementation of population diversity. A differentiated learning strategy based on fitness ranking divides individuals into high-quality and ordinary categories, implementing elite guidance and swarm intelligence learning, respectively. A hybrid evolutionary mechanism, integrating reverse learning driven by generalised opposition and perturbation based on the Cauchy distribution, substantially strengthens the algorithm’s resistance to entrapment in local optima. Moreover, dimension-masked crossover operations are introduced to greatly optimise the efficiency of population information sharing. Finally, through comparative experiments to verify the overall performance of IEHO on the CEC2017 test suite, compared with other competing algorithms, IEHO achieves the highest number of optimal solutions across multiple test functions. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

36 pages, 6369 KB

Open AccessArticle

Hybrid Nature-Inspired Optimization for the Cell Formation Problem with Machine Reliability and Alternative Routings

by Paulo Figueroa-Torrez, Broderick Crawford, Orlando Durán, Martín Jurado-Camacho, Dayana Roxana Andrade Roque, Adrian Vargas-Gutierrez and Felipe Cisternas-Caneo

Biomimetics 2026, 11(6), 387; https://doi.org/10.3390/biomimetics11060387 - 1 Jun 2026

Viewed by 672

Abstract

The Cell Formation Problem plays a fundamental role in cellular manufacturing due to its impact on efficiency, flexibility, and reliability. Its complexity increases under real-world conditions involving alternative process routes and machine reliability constraints, leading to the Generalized Cell Formation Problem with machine reliability. Researchers have classified the Cell Formation Problem as an NP-Hard problem. To address this computational complexity, this study presents a comparative and hybrid evaluation of the Black Widow Optimizer and the Golden Eagle Optimizer for the Generalized Cell Formation Problem with machine reliability, examining whether mechanisms derived from the Black Widow Optimizer can enhance the search behavior of the Golden Eagle Optimizer. The Black Widow Optimizer provides strong intensification through procreation, cannibalism, and mutation mechanisms, whereas the Golden Eagle Optimizer provides a balanced search process through its cruise and attack strategies. Experimental results show that the Black Widow Optimizer achieved better individual performance than the Golden Eagle Optimizer, with average RPD values of 0.855% and 1.068%, respectively. However, the hybrid strategy based on incorporating the mutation mechanism into the Golden Eagle Optimizer produced the best result, reaching an RPD of 0.592%. The study also employed the Wilcoxon–Mann–Whitney statistical test to validate the performance differences among algorithms, and the respective Big-O computational complexity was calculated. These findings highlight the potential of hybrid metaheuristics for designing robust and efficient manufacturing systems. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Graphical abstract

30 pages, 1977 KB

Open AccessArticle

Hybrid Neural Network Architecture for Automated Liver and Tumor Segmentation Using Ensemble Learning on CT Images

by Maryam Khoshkhabar, Saeed Meshgini and Reza Afrouzian

Biomimetics 2026, 11(6), 366; https://doi.org/10.3390/biomimetics11060366 - 25 May 2026

Cited by 1 | Viewed by 990

Abstract

Accurate and automatic segmentation of the liver and liver tumors from computed tomography (CT) images is essential for computer-assisted diagnosis, treatment planning, and clinical decision-making. Although deep learning-based segmentation models, particularly U-Net and its variants, have achieved promising results in medical image analysis, many existing approaches mainly focus on local pixel-level feature extraction and may have limited ability to explicitly model long-range spatial relationships among anatomically meaningful regions. In addition, liver tumor segmentation remains challenging due to low contrast, irregular tumor boundaries, heterogeneous tumor appearances, and noise or artifacts in CT images. To address these limitations, this study proposes a hybrid ensemble neural network architecture that integrates an improved U-Net and a Graph U-Net for automatic liver and liver tumor segmentation. The improved U-Net is designed to capture fine-grained local features and preserve detailed spatial information through an encoder–decoder structure with skip connections, while the Graph U-Net uses Simple Linear Iterative Clustering (SLIC)-based superpixels to construct a graph representation of CT images and model spatial dependencies between adjacent image regions. By combining these complementary representations through an ensemble learning strategy, the proposed framework enhances both pixel-level segmentation accuracy and robustness against noisy imaging conditions. The proposed method was evaluated on the LiTS17 dataset, where CT images were preprocessed using intensity filtering, resizing, data augmentation, and normalization. Experimental results demonstrate that the proposed ensemble architecture achieves 99.2% accuracy for liver segmentation and 98.1% accuracy for liver tumor segmentation, outperforming representative segmentation models such as MultiresUnet and R2U-Net. Furthermore, robustness experiments under different signal-to-noise ratio conditions show that the proposed model maintains stable performance in noisy CT images, achieving 85% accuracy even under severe noise at −4 dB SNR. This result highlights the advantage of integrating convolutional feature learning with graph-based spatial relationship modeling for improving segmentation stability when image quality is degraded by noise or artifacts. These findings indicate that the integration of improved U-Net, SLIC-based graph construction, and Graph U-Net provides an effective and noise-robust solution for liver and liver tumor segmentation, with potential applicability as a computer-assisted tool in clinical image analysis after further validation on larger and external datasets. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

21 pages, 2799 KB

Open AccessArticle

Dung Beetle with Reflection Cuckoo Catfish Optimizer for Numerical Optimization and Reservoir Production Optimization

by Shengnan Li and Taiju Yin

Biomimetics 2026, 11(5), 306; https://doi.org/10.3390/biomimetics11050306 - 30 Apr 2026

Viewed by 606

Abstract

As engineering systems grow in complexity, reliable metaheuristic optimizers are increasingly essential. While swarm intelligence algorithms are widely applied, recent approaches like the Cuckoo Catfish Optimizer (CCO) can experience premature convergence due to limited local exploitation and simplistic boundary handling. To address these limitations, this paper proposes the Dung Beetle with Reflection CCO (DBRCCO), integrating two principal mechanisms. First, an adaptive local search strategy inspired by dung beetle foraging is incorporated to intensify exploitation within dynamically contracting regions. Second, a momentum-preserving reflecting boundary mechanism replaces traditional clamping, maintaining population diversity near constraint edges. DBRCCO is evaluated against eight contemporary metaheuristic algorithms using the 29 CEC2017 benchmark functions and a reservoir production optimization problem. Statistical analyses indicate that DBRCCO achieves competitive performance, securing a Friedman ranking of 1.5172 (

p < 0.05

). In the reservoir application, DBRCCO improves the mean Net Present Value (NPV) by 12.54% while reducing variance by over 72% relative to the standard CCO. These findings suggest that DBRCCO offers a stable and effective alternative for complex optimization tasks. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

26 pages, 616 KB

Open AccessArticle

Enhancing Manufacturing Cell Formation Through Availability-Based Optimization Using the Black Widow Optimizer Metaheuristic

by Paulo Figueroa-Torrez, Orlando Duran, Broderick Crawford and Felipe Cisternas-Caneo

Biomimetics 2026, 11(5), 294; https://doi.org/10.3390/biomimetics11050294 - 23 Apr 2026

Cited by 1 | Viewed by 1052

Abstract

This study presents a multi-period Generalized Cell Formation Problem with Machine Availability (GCFP-MA) aimed at designing manufacturing cells that explicitly account for equipment reliability, maintainability, and temporal degradation. The proposed model extends classical formulations by introducing (i) availability-based constraints derived from Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR) and Markov-Chain models, (ii) downtime penalty costs reflecting non-production losses, and (iii) a multi-period horizon that captures system dynamics over time. To solve the resulting NP-hard problem, the Black Widow Optimizer (BWO)—a population-based metaheuristic inspired by cannibalistic reproduction—is implemented and validated against an exhaustive search benchmark. Computational experiments confirm that the BWO attains the global optimum with substantially reduced computational effort, achieving a balanced trade-off between exploration and exploitation. Results highlight that incorporating availability and repair dynamics prevents infeasible or over-optimistic configurations and yields cost-effective, robust cell layouts. The proposed approach provides both theoretical and practical contributions by integrating availability engineering and production system design within a unified optimization framework. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

30 pages, 2987 KB

Open AccessArticle

An Improved Biomimetic Beaver Behavior Optimizer for Inverse Kinematics of Rehabilitation Robotic Arms

by Shuxin Fan, Yonghong Deng and Zhibin Li

Biomimetics 2026, 11(4), 259; https://doi.org/10.3390/biomimetics11040259 - 8 Apr 2026

Cited by 1 | Viewed by 923

Abstract

Accurate inverse kinematics for rehabilitation robotic arms remains challenging because of strong nonlinearity, multiple feasible joint configurations, and strict joint-limit constraints. Inspired by the cooperative construction, adaptive exploration, and collective information-sharing behaviors of beavers, this study develops an improved biomimetic beaver behavior optimizer (IBBO) for optimization-based inverse kinematics solving. In the proposed framework, biologically inspired cooperative search is translated into an engineering-oriented numerical strategy through four complementary mechanisms: a strict elitist replacement with rollback to preserve population fitness consistency, a momentum-inspired information transfer scheme to accumulate effective search directions, a lightweight memetic coordinate-wise local search to strengthen late-stage exploitation, and an adaptive builder–disturbance schedule to progressively shift the search from exploration to refinement. The optimization capability of IBBO is first evaluated on the CEC2017 benchmark suite, where it demonstrates competitive accuracy and robustness. It is then applied to inverse kinematics solving for representative rehabilitation robotic arms by minimizing pose errors under joint constraints. The experimental results show that IBBO can consistently generate feasible joint solutions with improved terminal pose accuracy and stable convergence compared with baseline metaheuristics. Beyond numerical improvement, this study provides a biomimetic optimization framework that transfers beaver-inspired cooperative behaviors into rehabilitation robotics, offering an effective computational approach for constrained inverse kinematics problems. Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Figure 1

26 pages, 1349 KB

Open AccessArticle

ICOA: An Improved Coati Optimization Algorithm with Multi-Strategy Enhancement for Global Optimization and Engineering Design Problems

by Xiangyu Cheng, Min Zhou, Liping Zhang and Zikai Zhang

Biomimetics 2026, 11(4), 254; https://doi.org/10.3390/biomimetics11040254 - 7 Apr 2026

Viewed by 1006

Abstract

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017,

D = 100

), 1.000 (CEC2020,

D = 10

), and 2.208 (CEC2022,

D = 10

); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant (

p < 0.05

). Full article

(This article belongs to the Special Issue Advanced Nature-Inspired Optimization Algorithms)

► Show Figures

Journal Menu

Journal Browser

Advanced Nature-Inspired Optimization Algorithms

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (12 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI