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Recent Advances in Swarm Intelligence Algorithms and Their Applications, 2nd Edition
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Recent Advances in Swarm Intelligence Algorithms and Their Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 3931

Special Issue Editor

Prof. Dr. Jian Dong

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Central South University, Changsha 410075, China
Interests: swarm intelligence; antenna theory and design; microwave remote sensing; array signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

As a branch of artificial intelligence, swarm intelligence refers to the collective behavior of decentralized, self-organized systems. Swarm intelligence mainly attracts and organizes the local interactions among participants and with their environment. It aims to jointly cope with challenging tasks by means of competition, cooperation, and other independent or collaborative ways, especially complex system decision-making tasks in the open environment, which lead to the emergence of intelligent global behavior, unknown to individuals.

In recent years, the research community has witnessed an increased number of swarm intelligence algorithms that can efficiently solve complex computation tasks. Algorithms for optimization problems have been focused on, mainly due to the unprecedented complexity of problems arising from a diverse spectrum of domains, such as transportation, logistics, energy, climate, social networks, health, and Industry 4.0, among many others.

This Special Issue provides a platform for researchers from academia and industry to present their new and unpublished work and to promote future studies in swarm intelligence and its combination with real-world problems and other fields, including, but not limited to, antenna design, vehicle scheduling, drug design and discovery, image segmentation, feature selection, data clustering, traveling salesman problems, etc.

Prof. Dr. Jian Dong
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • swarm intelligence
  • evolutionary algorithms
  • optimization
  • metaheuristics
  • surrogate modeling
  • differential evolution
  • real-world applications
  • machine learning
  • optimal design
  • benchmark functions

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Related Special Issue

Published Papers (2 papers)

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Research

37 pages, 6077 KiB  
Article
MISAO: A Multi-Strategy Improved Snow Ablation Optimizer for Unmanned Aerial Vehicle Path Planning
by Cuiping Zhou, Shaobo Li, Cankun Xie, Panliang Yuan and Xiangfu Long
Mathematics 2024, 12(18), 2870; https://doi.org/10.3390/math12182870 - 14 Sep 2024
Viewed by 893
Abstract
The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article [...] Read more.
The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article suggests a multi-strategy improved (MISAO) snow ablation optimizer. It is employed in the unmanned aerial vehicle (UAV) path planning issue. To begin with, the tent chaos and elite reverse learning initialization strategies are merged to extend the diversity of the population; secondly, a greedy selection method is deployed to retain superior alternative solutions for the upcoming iteration; then, the Harris hawk (HHO) strategy is introduced to enhance the exploitation capability, which prevents trapping in partial ideals; finally, the red-tailed hawk (RTH) is adopted to perform the global exploration, which, enhances global optimization capability. To comprehensively evaluate MISAO’s optimization capability, a battery of digital optimization investigations is executed using 23 test functions, and the results of the comparative analysis show that the suggested algorithm has high solving accuracy and convergence velocity. Finally, the effectiveness and feasibility of the optimization path of the MISAO algorithm are demonstrated in the UAV path planning project. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications, 2nd Edition)
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28 pages, 972 KiB  
Article
Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems
by Omer Ali, Qamar Abbas, Khalid Mahmood, Ernesto Bautista Thompson, Jon Arambarri and Imran Ashraf
Mathematics 2023, 11(21), 4406; https://doi.org/10.3390/math11214406 - 24 Oct 2023
Cited by 2 | Viewed by 1274
Abstract
Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment [...] Read more.
Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment without parameters like the inertia weight w. The PPSO algorithm performs well for small-sized populations but needs improvements for large populations in the case of rapidly growing complex problems and dimensions. This study introduces a competitive coevolution process to enhance the capability of PPSO for global optimization problems. Competitive coevolution disintegrates the problem into multiple sub-problems, and these sub-swarms coevolve for a better solution. The best solution is selected and replaced with the current sub-swarm for the next competition. This process increases population diversity, reduces premature convergence, and increases the memory efficiency of PPSO. Simulation results using PPSO, fuzzy-dominance-based many-objective particle swarm optimization (FMPSO), and improved competitive multi-swarm PPSO (ICPPSO) are generated to assess the convergence power of the proposed algorithm. The experimental results show that ICPPSO achieves a dominating performance. The ICPPSO results for the average fitness show average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO. The Wilcoxon statistical significance test also confirms a significant difference in the performance of the ICPPSO, PPSO, and FMPSO algorithms at a 0.05 significance level. Full article
(This article belongs to the Special Issue Recent Advances in Swarm Intelligence Algorithms and Their Applications, 2nd Edition)
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