Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
3.4
Journals
Biomimetics
Special Issues
Nature-Inspired Metaheuristic Optimization Algorithms 2025
share announcement

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: 20 July 2025 | Viewed by 2939

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

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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
Viewed by 325
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)
►▼ Show Figures

Figure 1

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
Viewed by 317
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)
►▼ Show Figures

Figure 1

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 414
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)
►▼ Show Figures

Figure 1

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
Viewed by 907
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop