Particle Swarm Optimization with a new intensification strategy based on K-Means

被引:1
作者
Sag, Tahir [1 ]
Ihsan, Aysegul [2 ]
机构
[1] Selcuk Univ, Fac Technol, Comp Engn, Konya, Turkiye
[2] Selcuk Univ, Fac Technol, Informat Technol Engn, Konya, Turkiye
来源
PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI | 2023年 / 29卷 / 03期
关键词
Intensification strategy; K-Means; PSO; ALGORITHM; PSO;
D O I
10.5505/pajes.2022.37898
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Particle Swarm Optimization (PSO) is a swarm intelligence-based metaheuristic algorithm inspired by the foraging behaviors of fish or birds. Despite the advantages of having a simple and effective working structure, PSO also has some disadvantages, such as early convergence, getting trapped in local minima, and weak global search capabilities. In this study, a novel intensification strategy based on K-Means clustering has been proposed to enhance the performance of PSO. The proposed method is called Particle Swarm Optimization with a New Intensification Strategy based on K-Means (PSO-ISK). In the first step of PSO-ISK, particles in PSO are grouped into different clusters. Then, a center and the farthest particle from the center are identified for each cluster. PSO-ISK proposes a new intensification strategy by improving the results of the farthest particle from the center. The performance of PSO-ISK is analyzed using 16 different benchmark test functions. The obtained results are compared with Standard PSO (SPSO) and 7 different PSO variants. According to the comparison results, PSO-ISK provides a notable performance improvement by outperforming SPSO and all seven PSO variants. The comparisons conducted have proven that PSO-ISK produces more effective outcomes than other studies, which results in a significant contribution to improving performance.
引用
收藏
页码:264 / 273
页数:10
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