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Biomimetics
Special Issues
Application of Nature-Inspired Algorithms and Technologies in Engineering
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Application of Nature-Inspired Algorithms and Technologies in Engineering

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

Deadline for manuscript submissions: 30 January 2027 | Viewed by 1398

Special Issue Editor

Dr. Noune Melkoumian

E-Mail Website
Guest Editor
Faculty of Sciences, Engineering and Technology, The University of Adelaide, Adelaide, SA 5005, Australia
Interests: geomechanics; geotechnical engineering; fracture mechanics; active structural health monitoring; applied mathematics and machine learning (supervised and semi-supervised); behavioural based OHS

Special Issue Information

Dear Colleagues,

Nature has evolved highly efficient strategies over millions of years, offering various sources of inspiration for solving complex engineering challenges.

This Special Issue seeks to showcase innovative research that harnesses nature-inspired algorithms and biomimetic designs or technologies to advance engineering designs and systems across diverse domains. We welcome contributions that explore how principles observed in nature—such as optimization, self-organization, adaptability, and resilience—can be translated into practical applications in engineering, including mining, mechanical, civil, environmental, aerospace, and many other fields.

Topics of interest include, but are not limited to, the following: swarm intelligence and evolutionary algorithms for optimization and control; bio-inspired structural and material systems; environmentally adaptive technologies; biomimetic ventilation systems; bioinspired robots for mining and exploration; and nature-inspired sustainable infrastructures. Theoretical advances, conceptual designs and numerical simulations are encouraged, along with experimental validations and case studies demonstrating real-world implementation of nature-inspired solutions. By integrating biological insights with cutting-edge technologies, this Special Issue aims to bridge disciplines and highlight the potential of nature-inspired approaches to address pressing engineering problems. Researchers, practitioners, and interdisciplinary teams are invited to contribute original articles, reviews, and perspectives that advance this exciting and rapidly evolving field.

Dr. Noune Melkoumian
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

  • nature-inspired algorithms
  • biomimetic technologies
  • optimization
  • self-organization
  • adaptability
  • swarm intelligence
  • bioinspired structures

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

Published Papers (2 papers)

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Research

35 pages, 2640 KB  
Article
Optimizing the Classic and the Energy-Efficient Permutation Flowshop Scheduling Problem with a Hybrid Tyrannosaurus Rex Optimization Algorithm
by Maria Tsiftsoglou, Yannis Marinakis and Magdalene Marinaki
Biomimetics 2026, 11(4), 262; https://doi.org/10.3390/biomimetics11040262 - 10 Apr 2026
Viewed by 402
Abstract
This paper introduces a Hybrid Tyrannosaurus Rex Optimization Algorithm (Hybrid TROA) combined with Variable Neighborhood Search (VNS), two variations of the Path Relinking strategy, and a randomized Nawaz–Enscore–Ham (NEH) heuristic to address the Permutation Flowshop Scheduling Problem (PFSP). The TROA is a novel [...] Read more.
This paper introduces a Hybrid Tyrannosaurus Rex Optimization Algorithm (Hybrid TROA) combined with Variable Neighborhood Search (VNS), two variations of the Path Relinking strategy, and a randomized Nawaz–Enscore–Ham (NEH) heuristic to address the Permutation Flowshop Scheduling Problem (PFSP). The TROA is a novel bio-inspired meta-heuristic algorithm modeled on the hunting behavior of the prehistoric Tyrannosaurus Rex. Leveraging the potential of this newly developed and efficient algorithm, we propose a framework in which an initial population of solutions is generated using the randomized NEH heuristic. These solutions are then further optimized through VNS and Path Relinking, yielding highly satisfactory results for the PFSP. First, we consider two optimization criteria separately: the makespan and the total flow time. Next, we conduct a comparative study of the Hybrid TROA against other prominent meta-heuristics, along with a statistical analysis using non-parametric tests, to determine the best-performing method for each objective. According to our findings, the Hybrid TROA proves to be the most suitable method in this study for minimizing both targets. Finally, recognizing that contemporary industry demands both high productivity and energy efficiency, we propose an energy-efficient version of the classic PFSP, simultaneously considering two criteria for optimization: the makespan and total energy consumption. Our study introduces a novel objective function that achieves balanced optimization by integrating both criteria. Full article
(This article belongs to the Special Issue Application of Nature-Inspired Algorithms and Technologies in Engineering)
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26 pages, 986 KB  
Article
Aquila Optimization-Assisted Artificial Neural Network for Classification Problems
by Gokhan Kayhan and Seyma Hasbolat Unal
Biomimetics 2026, 11(4), 240; https://doi.org/10.3390/biomimetics11040240 - 2 Apr 2026
Viewed by 618
Abstract
Artificial Neural Networks (ANNs) are models that learn patterns in input-output data. Since traditional optimization methods often get trapped in local optima when determining weight and bias values, identifying optimal parameters and enhancing network performance remain significant research areas. Heuristic algorithms are also [...] Read more.
Artificial Neural Networks (ANNs) are models that learn patterns in input-output data. Since traditional optimization methods often get trapped in local optima when determining weight and bias values, identifying optimal parameters and enhancing network performance remain significant research areas. Heuristic algorithms are also generally used in solving optimization problems and are used to train ANNs. In the study, the parameter optimization of the ANN model was carried out using the Aquila Optimizer (AO), a recent metaheuristic algorithm, and a hybrid Aquila Optimizer optimized ANN model (AOANN) was proposed. Hybridization of algorithms contributes to the improvement of optimization performance. In this study, the proposed model was assessed on empirical datasets, including Cancer, Iris, Glass, and Wine, and its performance was compared with that of well-established ANN models. The results of the evaluation revealed that the proposed AOANN, a soft computation model, demonstrated stability in solving classification problems. Full article
(This article belongs to the Special Issue Application of Nature-Inspired Algorithms and Technologies in Engineering)
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