Particle Swarm Optimization: Advances, Applications, and Experimental Insights  

作　　者：Laith Abualigah 

机构地区：[1]Computer Science Department,Al al-Bayt University,Mafraq,25113,Jordan

出　　处：《Computers, Materials & Continua》2025年第2期1539-1592,共54页计算机、材料和连续体(英文)

摘　　要：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 weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.

关 键 词：Particle swarm optimization(PSO) optimization algorithms data mining machine learning engineer-ing design energy systems healthcare applications ROBOTICS comparative analysis algorithm performance evaluation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]