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: 31 August 2024 | Viewed by 1591

Special Issue Editor


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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
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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This Special Issue provides a platform for researchers from academia and industry to present their new and unpublished work and to promote future studies in swarm intelligence and its combination with real-world problems and other fields, including, but not limited to, antenna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

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Keywords

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

Published Papers (1 paper)

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Research

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
Viewed by 766
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
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