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Biomimetics
Special Issues
Bio-Inspired Optimization Algorithms and Designs for Engineering Applications
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Special Issue "Bio-Inspired Optimization Algorithms and Designs for Engineering Applications"

A special issue of Biomimetics (ISSN 2313-7673).

Deadline for manuscript submissions: 30 September 2023 | Viewed by 1723

Special Issue Editors

Prof. Dr. Heming Jia

E-Mail Website
Guest Editor
Department of Information Engineering, Sanming University, Sanming 365004, China
Interests: optimization; Remora Optimization Algorithm (ROA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems
Special Issues, Collections and Topics in MDPI journals
Dr. Laith Abualigah

E-Mail Website
Guest Editor
Faculty of Information Technology, Al Al-Bayt University, Mafraq, Jordan
Interests: arithmetic optimization algorithm (AOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling; optimization algorithms; evolutionary computations; information retrieval; text clustering; feature selection; combinatorial problems; optimization; advanced machine learning; big data; natural language processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xuewen Xia

E-Mail Website
Guest Editor
College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, China
Interests: particle swarm optimization (PSO); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; feature selection; combinatorial problems

Special Issue Information

Dear Colleagues,

With the development in industrialization, engineering applications are becoming more and more frequent. Thereby, wide and various engineering problems come with it. To solve these complex real-world problems, a host of optimization algorithms are proposed, and bio-inspired optimization algorithms account for a large proportion. Numerous literature shows that bio-inspired optimization algorithms with the capability of rapidly converging and escaping from local optimal could solve complex problems, such as non-convex, nonlinear constraints, and high-dimensional problems. Due to the sufficient performance of these optimization algorithms, through an exploration and exploitation process, accurate and adequate results can eventually be produced at a small cost.

The purpose of this Special Issue is to capture recent contributions of high-quality papers focusing on interdisciplinary research on the optimization algorithm for engineering applications using methods that inspired by the dynamic and intelligent conducts of creatures, such as hunting, mating, and other social behaviors. We invite the researchers to submit their original contributions addressing particular challenging aspects of bio-inspired optimization algorithms from theoretical and applied viewpoints. The topics of this Special Issue include (but are not limited to) the following:

  • Bio-Inspired Optimization Algorithms
  • Optimization algorithms
  • Meta-heuristics
  • Swarm intelligence
  • Engineering applications
  • Engineering design problems
  • Real-world applications
  • Feature selection
  • Image segmentation
  • Constraint handling
  • Benchmarks
  • Novel Approaches
  • Complicated Optimization Problems
  • Industrial Problems.

Prof. Dr. Heming Jia
Dr. Laith Abualigah
Prof. Dr. Xuewen Xia
Guest Editors

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 quarterly 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 1800 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 optimization algorithms
  • optimization algorithms
  • engineering application
  • metaheuristic algorithms
  • soft computing

Published Papers (2 papers)

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Article
Augmented Harris Hawks Optimizer with Gradient-Based-Like Optimization: Inverse Design of All-Dielectric Meta-Gratings
by
Kofi Edee
Biomimetics 2023, 8(2), 179; https://doi.org/10.3390/biomimetics8020179 - 24 Apr 2023
Viewed by 600
Abstract
In this paper, we introduce a new hybrid optimization method for the inverse design of metasurfaces, which combines the original Harris hawks optimizer (HHO) with a gradient-based optimization method. The HHO is a population-based algorithm that mimics the hunting process of hawks tracking [...] Read more.
In this paper, we introduce a new hybrid optimization method for the inverse design of metasurfaces, which combines the original Harris hawks optimizer (HHO) with a gradient-based optimization method. The HHO is a population-based algorithm that mimics the hunting process of hawks tracking prey. The hunting strategy is divided into two phases: exploration and exploitation. However, the original HHO algorithm performs poorly in the exploitation phase and may get trapped and stagnate in a basin of local optima. To improve the algorithm, we propose pre-selecting better initial candidates obtained from a gradient-based-like (GBL) optimization method. The main drawback of the GBL optimization method is its strong dependence on initial conditions. However, like any gradient-based method, GBL has the advantage of broadly and efficiently spanning the design space at the cost of computation time. By leveraging the strengths of both methods, namely GBL optimization and HHO, we show that the proposed hybrid approach, denoted as GBL–HHO, is an optimal scenario for efficiently targeting a class of unseen good global optimal solutions. We apply the proposed method to design all-dielectric meta-gratings that deflect incident waves into a given transmission angle. The numerical results demonstrate that our scenario outperforms the original HHO. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)
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Article
Progressive Learning Hill Climbing Algorithm with Energy-Map-Based Initialization for Image Reconstruction
by
Yuhui Zhang
,
Wenhong Wei
and
Zijia Wang
Biomimetics 2023, 8(2), 174; https://doi.org/10.3390/biomimetics8020174 - 22 Apr 2023
Viewed by 557
Abstract
Image reconstruction is an interesting yet challenging optimization problem that has several potential applications. The task is to reconstruct an image using a fixed number of transparent polygons. Traditional gradient-based algorithms cannot be applied to the problem since the optimization objective has no [...] Read more.
Image reconstruction is an interesting yet challenging optimization problem that has several potential applications. The task is to reconstruct an image using a fixed number of transparent polygons. Traditional gradient-based algorithms cannot be applied to the problem since the optimization objective has no explicit expression and cannot be represented by computational graphs. Metaheuristic search algorithms are powerful optimization techniques for solving complex optimization problems, especially in the context of incomplete information or limited computational capability. In this paper, we developed a novel metaheuristic search algorithm named progressive learning hill climbing (ProHC) for image reconstruction. Instead of placing all the polygons on a blank canvas at once, ProHC starts from one polygon and gradually adds new polygons to the canvas until reaching the number limit. Furthermore, an energy-map-based initialization operator was designed to facilitate the generation of new solutions. To assess the performance of the proposed algorithm, we constructed a benchmark problem set containing four different types of images. The experimental results demonstrated that ProHC was able to produce visually pleasing reconstructions of the benchmark images. Moreover, the time consumed by ProHC was much shorter than that of the existing approach. Full article
(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications)
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