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Biomimetics
Special Issues
Bio-Inspired Algorithms and Systems: From Nature’s Concepts to Scalable Solutions
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Bio-Inspired Algorithms and Systems: From Nature’s Concepts to Scalable Solutions

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

Deadline for manuscript submissions: 25 February 2027 | Viewed by 481

Editor

Dr. Abdel F. Isakovic

E-Mail Website
Guest Editor
Physics Department, Illinois Wesleyan University, 1312 N. Park St., Bloomington, IL 61702, USA
Interests: bio-inspired computing and networks (particle swarm optimization, ant colony optimization, genetic algorithm, neural networks); condensed matter and statistical physics; education research methods

Special Issue Information

Dear Biomimetics Community,

The progress in developing and utilizing biologically inspired algorithms and devices has been tremendous in recent decades, and new opportunities for computational, experimental, and theoretical work exist. Algorithmic insights from the collective behavior of living organisms are a continuous source of inspiration for the non-biological world, both for the development of novel hardware processes and for algorithm development and improvement. New challenges include, but are not limited to, hardware implementations of bio-inspired algorithms, optimization of bio-inspired systems and devices, and novel concepts that originate from the crossover between energy optimization and bio-inspired design.

For this Special Issue, experimental, computational, and computation-experiment crossover papers are expected (*).

This Special Issue of Biomimetics is focused on the following themes:

  • Integration of swarm intelligence optimization algorithms with hardware and in experimental situations where it might be feasible;
  • Functional (hardware) and algorithmic (software) adaptability of smart matter;
  • Energy, time, and trajectory optimized motion in 2D and 3D, based on biology-inspired algorithms, and their hybrids;
  • Energy-efficient actuators based on elastomers, hydrogels, liquid crystals, etc.;
  • Comparison of different hybrid algorithms with respect to the range and boundaries of applications;
  • Emergent functionality in complex systems at the edge and out of equilibrium;
  • Open questions and future directions in algorithm-to-experiment and experiment-to-algorithm crossover in biomimetics.

(*) In exceptional cases, we will consider a select, well-outlined review paper after approval of a 2-page outline.

Dr. Abdel F. Isakovic
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-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

  • smart, adaptable algorithms, matter, and actuators
  • bio-inspired, energy-efficient systems and devices
  • bio-inspired algorithm-to-experiment systems
  • emergent complex systems

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

Published Papers (2 papers)

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33 pages, 8099 KB  
Article
A Multi-Strategy Improved Dung Beetle Optimizer for High-Dimensional Optimization and Engineering Applications
by Shuxin Wang, Yinggao Yue and Mengji Xiong
Biomimetics 2026, 11(7), 485; https://doi.org/10.3390/biomimetics11070485 - 10 Jul 2026
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Abstract
When addressing high-dimensional complex optimization problems, the vanilla Dung Beetle Optimizer (DBO) suffers from slow convergence, frequent stagnation in local optima, and progressive degradation of population diversity. To overcome the above inherent defects, this paper proposes a multi-strategy hybrid improved DBO variant named [...] Read more.
When addressing high-dimensional complex optimization problems, the vanilla Dung Beetle Optimizer (DBO) suffers from slow convergence, frequent stagnation in local optima, and progressive degradation of population diversity. To overcome the above inherent defects, this paper proposes a multi-strategy hybrid improved DBO variant named the SWDBO, which incorporates three targeted enhancement modules. First, an adaptive population proportion strategy is developed to dynamically adjust the population sizes of rolling beetles, brood beetles, small beetles and thief beetles throughout iterations. More individuals are allocated for extensive global exploration at the early evolutionary stage, while more search agents are reserved for delicate local exploitation in later iterations, which maintains stable population diversity over the entire optimization process. Second, the bubble-net encircling and spiral predation mechanisms of the Whale Optimization Algorithm (WOA) are embedded into the position update formula of rolling beetles. This integration strengthens fine local search performance and accelerates the overall convergence rate. Third, a modified seagull optimization operator combined with Lévy random perturbation is introduced into the position updating rule of thief beetles. This improved jump mechanism optimizes individual movement trajectories and enables the algorithm to effectively escape local optimal traps. Numerical experiments are implemented on the 100-dimensional benchmark functions of CEC2017 and CEC2020. Moreover, the proposed SWDBO is validated on three classical constrained engineering optimization tasks, including three-bar truss design, ten-bar truss design and cantilever beam sizing optimization. Wilcoxon rank-sum tests statistically verify significant performance disparities between the SWDBO and competing optimizers. For the three structural engineering cases, the design solutions obtained by the SWDBO produce lighter structural mass while satisfying all constraint requirements. Overall experimental evidence proves that the proposed multi-strategy improvement framework can efficiently tackle high-dimensional numerical optimization and constrained engineering design problems, and the SWDBO exhibits prominent performance in balancing global exploration and local exploitation. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms and Systems: From Nature’s Concepts to Scalable Solutions)
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40 pages, 449 KB  
Systematic Review
Machine Learning for Graduation Prediction in Higher Education: A Systematic Review with a Bio-Inspired Optimization Perspective
by Andrés Yáñez, Broderick Crawford, Eric Monfroy, Álex Paz, José Barrera-García, Felipe Cisternas-Caneo, Álvaro Peña Fritz and Ricardo Soto
Biomimetics 2026, 11(7), 512; https://doi.org/10.3390/biomimetics11070512 - 21 Jul 2026
Abstract
Timely graduation, time-to-degree, and degree completion are key indicators of student progression and institutional effectiveness in higher education. This study presents a PRISMA-based systematic literature review of machine learning approaches for graduation-related prediction, with attention to predictive targets, pipeline components, scalability, and bio-inspired [...] Read more.
Timely graduation, time-to-degree, and degree completion are key indicators of student progression and institutional effectiveness in higher education. This study presents a PRISMA-based systematic literature review of machine learning approaches for graduation-related prediction, with attention to predictive targets, pipeline components, scalability, and bio-inspired optimization. Searches in Web of Science Core Collection and Scopus identified 278 records, of which 25 studies published between 2021 and 2025 met the eligibility criteria. The findings show that most studies formulated graduation prediction as a supervised classification task, relied heavily on academic performance variables, and frequently used tree-based or ensemble models. Feature selection, explainability, and hyperparameter optimization were commonly reported, but bio-inspired optimization was actively implemented in only two studies through Particle Swarm Optimization, Genetic Algorithms, or Ant Colony Optimization. The evidence base also remains limited in scalability, as most studies used single-institution datasets and provided little external validation. These findings identify an opportunity for Bio-Inspired Educational Analytics through scalable feature selection, efficient hyperparameter optimization, model simplification, and multi-objective trade-off analysis. Future research should evaluate whether lightweight, hybrid, and multi-objective metaheuristics can support accurate, interpretable, fair, and transferable graduation prediction systems. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms and Systems: From Nature’s Concepts to Scalable Solutions)
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