Particle Swarm Optimization: Advances and Applications

Submission Deadline: 31 March 2025 View: 3002 Submit to Special Issue

Guest Editors

Dr. Diego Oliva, Universidad de Guadalajara, Mexico
Dr. Mario A. Navarro, Universidad de Guadalajara, Mexico

Summary

The field of Particle Swarm Optimization (PSO) has experienced significant growth and innovation, driving progress in solving complex optimization problems in a variety of domains. Inspired by the social behavior of birds and fish, PSO is a powerful and versatile optimization technique used in engineering, computer science, and other fields. This special issue focuses on the latest advances in PSO and its various applications, highlighting its impact on solving real-world problems with high efficiency and effectiveness. The collection includes studies exploring new variations and improvements in PSO algorithms, theoretical analyses, and practical implementations. Applications cover many areas, such as robotics, automation, and healthcare and medical applications. This issue also presents reviews and studies that provide an overview of the current state and future potential of PSO, making it a valuable resource for researchers and experts.

Keywords

Particle Swarm Optimization, Optimization algorithms, Engineering applications, Computational intelligence, Theoretical analysis, Algorithm improvements

Published Papers

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Open Access

REVIEW

Particle Swarm Optimization: Advances, Applications, and Experimental Insights
Laith Abualigah
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1539-1592, 2025, DOI:10.32604/cmc.2025.060765
（This article belongs to the Special Issue: Particle Swarm Optimization: Advances and Applications)
Abstract Particle Swarm Optimization (PSO) has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields. This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications, but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms. Covering six strategic areas, which include Data Mining, Machine Learning, Engineering Design, Energy Systems, Healthcare, and Robotics, the study demonstrates the versatility and effectiveness of the PSO. Experimental results are, however, used to show the strong and More >

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ARTICLE

Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations
Jiaying Shen, Donglin Zhu, Yujia Liu, Leyi Wang, Jialing Hu, Zhaolong Ouyang, Changjun Zhou, Taiyong Li
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 345-369, 2025, DOI:10.32604/cmc.2024.060335
（This article belongs to the Special Issue: Particle Swarm Optimization: Advances and Applications)
Abstract The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life. The development of the Internet of Things (IoT) relies on the support of base stations, which provide a solid foundation for achieving a more intelligent way of living. In a specific area, achieving higher signal coverage with fewer base stations has become an urgent problem. Therefore, this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization (EPSO)… More >

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Particle Swarm Optimization: Advances and Applications

Guest Editors

Summary

Keywords

Published Papers

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869

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471

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1019

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580

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