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Biomimetics
Special Issues
Bio-Inspired Optimization Algorithms
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Bio-Inspired Optimization Algorithms

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2157

Special Issue Editor

Prof. Dr. Ali Akdagli

E-Mail Website
Guest Editor
Department of Electrical-Electronics Engineering, Mersin University, 33110 Mersin, Turkiye
Interests: wireless communication systems; computer networks and security; artificial Intelligence

Special Issue Information

Dear Colleagues,

We are facing increasingly complex and multi-dimensional problems in today's world. The search for effective solutions to these challenging issues has led to the development of bio-inspired optimization algorithms. These algorithms mimic the elegant and adaptive problem-solving strategies we see in natural systems—like the cooperation of ant colonies, the coordinated movement of bird flocks, evolutionary processes, and the learning mechanisms of neural networks. They offer powerful and flexible tools for navigating vast search spaces where traditional methods often fall short. 

The goal of this Special Issue is to bring together the latest research and comprehensive reviews in the field of nature-inspired optimization. We aim to explore both the theoretical foundations and transformative applications of these nature-derived metaheuristic methods. We welcome contributions that present novel algorithm designs, deepen theoretical understanding (convergence, complexity, hybridization), or demonstrate innovative applications across various disciplines. These include engineering design, logistics and scheduling, data mining, financial modeling, energy management, machine learning model tuning, and optimization of sustainable systems, among others. 

To provide a structured and comprehensive perspective, this Special Issue will cover the following key areas: 

Part I: Algorithmic Foundations and Advancements 

This section focuses on the development, analysis, and improvement of core algorithms. Topics include the following:

  • Swarm intelligence (e.g., particle swarm optimization, ant colony optimization, artificial bee colony);
  • Evolutionary and genetic algorithms;
  • Physics- and chemistry-based algorithms (e.g., simulated annealing, gravitational search);
  • Neural-inspired and brainstorm optimization;
  • Theoretical analyses of convergence, parameter tuning, and hybridization techniques;
  • Multi-objective and constrained optimization frameworks. 

Part II: Interdisciplinary Applications and Case Studies 

This section highlights the practical impact of bio-inspired algorithms in solving domain-specific challenges. Topics include the following:

  • Engineering design and automation;
  • Scheduling, routing, and logistics;
  • Image processing, feature selection, and data clustering;
  • Power systems, renewable energy integration, and smart grids;
  • Bioinformatics and biomedical engineering;
  • Financial forecasting and portfolio optimization;
  • Deep learning architecture search and hyperparameter optimization. 

We believe this collection will serve as a valuable resource for both researchers and practitioners, strengthening the bridge between biological inspiration and technological innovation. By showcasing the latest paradigms and successful applications, we hope to encourage interdisciplinary dialog and discovery in this dynamic field. 

We look forward to receiving your valuable contributions.

Prof. Dr. Ali Akdagli
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-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-inspiration
  • swarm intelligence
  • particle swarm optimization
  • ant colony optimization
  • artificial bee colony
  • evolutionary algorithms
  • neural-inspired optimization
  • brainstorm optimization

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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

44 pages, 2643 KB  
Article
An Improved Genghis Khan Shark Optimization Algorithm for Solving Optimization Problems
by Yanjiao Wang and Jiaqi Wang
Biomimetics 2026, 11(4), 270; https://doi.org/10.3390/biomimetics11040270 - 14 Apr 2026
Viewed by 330
Abstract
As an innovative metaheuristic algorithm, Genghis Khan Shark Optimization (GKSO) faces challenges, including a tendency towards local optima and poor convergence speed and accuracy. To mitigate these limitations, an improved Genghis Khan shark optimizer (IGKSO) is proposed in this paper. A population partitioning [...] Read more.
As an innovative metaheuristic algorithm, Genghis Khan Shark Optimization (GKSO) faces challenges, including a tendency towards local optima and poor convergence speed and accuracy. To mitigate these limitations, an improved Genghis Khan shark optimizer (IGKSO) is proposed in this paper. A population partitioning method based on cosine similarity and fitness is introduced, where individuals are strategically assigned to different evolutionary phases: Disadvantaged populations are responsible for the foraging stage. By contrast, advantaged populations dominate the moving stage. In the moving stage, the base vector is randomly selected from multiple candidates, which ensures the evolutionary direction of the population while maintaining its diversity. An adaptive step-size mechanism is introduced to avoid boundary overflow problems. A subspace method is employed to prevent diversity loss during foraging. Additionally, in the hunting stage, a novel opposition-based learning strategy is proposed to moderate the tendency of converging to suboptimal solutions. Furthermore, during the self-protection phase, a criterion for assessing the diversity of the whole population is employed to monitor and supplement diversity in real time. The results of the CEC2017 and CEC2019 benchmark test sets reveal that IGKSO exhibits substantial advantages over the GKSO algorithm and eight other high-performance algorithms in terms of convergence speed and accuracy. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms)
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16 pages, 1322 KB  
Article
Chaos-Embedded Multi-Objective Intelligent Optimization-Based Explainable Classification Model for Determining Cherry Fruit Fly Infestation Levels Using Pomological Data
by Suna Yildirim, Inanc Ozgen, Bilal Alatas and Hakan Yildirim
Biomimetics 2026, 11(3), 218; https://doi.org/10.3390/biomimetics11030218 - 18 Mar 2026
Viewed by 608
Abstract
The European cherry fruit fly (Rhagoletis cerasi L.) poses a significant pest threat to cherry production due to its rapid reproduction and host specificity, causing substantial economic damage. This study presents a novel, explainable, and biologically inspired data-driven classification model based on [...] Read more.
The European cherry fruit fly (Rhagoletis cerasi L.) poses a significant pest threat to cherry production due to its rapid reproduction and host specificity, causing substantial economic damage. This study presents a novel, explainable, and biologically inspired data-driven classification model based on fruit characteristics to support targeted and sustainable pest control strategies. In research conducted at four different locations in Elazığ province, three population classes were determined based on the number of adult individuals caught in traps, and 10 different fruit characteristics were measured in fruit samples belonging to each class. The data used in this study are original data obtained by the authors. To examine the relationship between pomological characteristics of cherry fruit and cherry fruit fly density, the Chaotic Rule-based–Strength Pareto Evolutionary Algorithm2 (CRb-SPEA2) method, developed as a multi-objective and chaos-integrated evolutionary rule mining framework, was adapted. The developed algorithm aimed for high performance, interpretability, and transparency. Accuracy, Precision, and Recall metrics, which are conflicting objectives, were optimized with Pareto-optimal solutions, yielding selectable results for domain experts. To increase population diversity and reduce the risk of early convergence and getting stuck in a local optimum, the Tent chaotic mapping mechanism was also integrated into the system. Furthermore, the model was trained without the need for predefined automatic discretization of the continuous value ranges of the attributes. The proposed model achieved superior results across all classes, with the highest accuracy rate of 82.6% recorded in the High class, demonstrating excellent sensitivity and recall values. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms)
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Graphical abstract

21 pages, 9175 KB  
Article
Multi-Objective Grey Wolf Optimizer-Tuned LQR Attitude Control of a Three-DOF Hover System
by Abdullah Çakan
Biomimetics 2026, 11(3), 215; https://doi.org/10.3390/biomimetics11030215 - 17 Mar 2026
Viewed by 712
Abstract
Attitude control of unmanned aerial vehicles is a problem that needs to be solved in a reliable manner. The research presented in this paper examines a systematic approach to the design of an LQR state feedback controller for the three-DOF hover system. The [...] Read more.
Attitude control of unmanned aerial vehicles is a problem that needs to be solved in a reliable manner. The research presented in this paper examines a systematic approach to the design of an LQR state feedback controller for the three-DOF hover system. The state space model is used to derive the feedback gain K, with the diagonal elements of the weighting matrices Q and R used as design variables. A multi-objective grey wolf optimizer is used to obtain Q–R matrices based on closed-loop simulations under representative roll, pitch and yaw reference commands. There are four separate multi-objective optimization runs, each using one of four standard error indices which are the integral of absolute error (IAE), the integral of time-weighted absolute error (ITAE), the integral of squared error (ISE) and the integral of time-weighted squared error (ITSE). Each index is used to track roll, pitch and yaw errors at the same time and the resulting non-dominated solution sets are post-processed using TOPSIS to select a compromise knee-point design. The simulation results show that the adjusted LQR parameters lead to feasible tracking performance. The proposed framework provides a systematic and replicable method for LQR weight selection in hover-type attitude control problems under the considered simulation settings. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms)
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