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Biomimetics
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Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and...
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Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and Computing

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1416

Special Issue Editor

Dr. AbdElRahman Ahmed ElSaid

E-Mail Website
Guest Editor
Department of Computer Science, University of North Carolina Wilmington, Wilmington, NC, USA
Interests: machine learning; data science; neural architecture search; neuro-evolution; data mining; meta-heuristics

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue on “Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and Computing” in Biomimetics. Nature-inspired approaches—such as evolutionary algorithms, swarm intelligence, and biologically inspired optimization—have proven to be powerful tools for addressing complex computational challenges. These methods are widely used in artificial intelligence and engineering to develop adaptive, robust, and efficient systems.

This Special Issue aims to bring together cutting-edge research on how principles from evolution and natural systems inform AI methods, with a particular emphasis on their application in engineering domains. Topics of interest include advances in evolutionary computation, hybrid frameworks that integrate nature-inspired algorithms with machine learning, and practical applications ranging from robotics and autonomous systems to optimization and control. Interdisciplinary contributions that explore how bio-inspired algorithms can solve real-world engineering problems are especially encouraged.

In this Special Issue, we welcome original research articles and reviews. Research areas may include, but are not limited to, the following:

  • Evolutionary algorithms and optimization;
  • Swarm intelligence and collective behavior;
  • Bio-inspired control systems;
  • Robotics and autonomous systems;
  •  Hybrid machine learning frameworks;
  • Computational intelligence;
  • Nature-inspired engineering applications.

We look forward to receiving your contributions.

Dr. AbdElRahman Ahmed ElSaid
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

  • evolutionary algorithms
  • nature-inspired computing
  • swarm intelligence
  • bio-inspired optimization
  • robotics
  • control systems
  • machine learning
  • computational intelligence
  • autonomous systems
  • engineering applications

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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

42 pages, 26475 KB  
Article
A Novel Elite-Guided Hybrid Metaheuristic Algorithm for Efficient Feature Selection
by Zichuan Chen, Bin Fu and Yangjian Yang
Biomimetics 2025, 10(11), 747; https://doi.org/10.3390/biomimetics10110747 - 6 Nov 2025
Viewed by 573
Abstract
Feature selection aims to identify a relevant subset of features from the original feature set to enhance the performance of machine learning models, which is crucial for improvig model accuracy. However, this task is highly challenging due to the enormous search space, often [...] Read more.
Feature selection aims to identify a relevant subset of features from the original feature set to enhance the performance of machine learning models, which is crucial for improvig model accuracy. However, this task is highly challenging due to the enormous search space, often requiring the use of meta-heuristic algorithms to efficiently identify near-optimal feature subsets. This paper proposes an improved algorithm based on Northern Goshawk Optimization (NGO), called Elite-guided Hybrid Northern Goshawk Optimization (EH-NGO), for feature selection tasks. The algorithm incorporates an elite-guided strategy within the NGO framework, leveraging information from elite individuals to direct the population’s evolutionary trajectory. To further enhance population diversity and prevent premature convergence, a vertical crossover mutation strategy is adopted, which randomly selects two different dimensions of an individual for arithmetic crossover to generate new solutions, thereby improving the algorithm’s global exploration capability. Additionally, a boundary control strategy based on the global best solution is introduced to reduce ineffective searches and accelerate convergence. Experiments conducted on 30 benchmark functions from the CEC2017 and CEC2022 test set demonstrate the superiority of EH-NGO in global optimization, outperforming eight compared state-of-the-art algorithms. Furthermore, a novel feature selection method based on EH-NGO is proposed and validated on 22 datasets of varying scales. Experimental results show that the proposed method can effectively select feature subsets that contribute to improved classification performance. Full article
(This article belongs to the Special Issue Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and Computing)
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44 pages, 9238 KB  
Article
SZOA: An Improved Synergistic Zebra Optimization Algorithm for Microgrid Scheduling and Management
by Lihong Cao and Qi Wei
Biomimetics 2025, 10(10), 664; https://doi.org/10.3390/biomimetics10100664 - 1 Oct 2025
Cited by 3 | Viewed by 630
Abstract
To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with [...] Read more.
To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article
(This article belongs to the Special Issue Evolutionary and Nature-Inspired AI: Bridging the Gap Between Engineering and Computing)
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