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

Authors : Tahir SAG, Aysegul IHSAN

Pages : 264-273

View : 20 | Download : 27

Publication Date : 2023-06-27

Article Type : Research Paper

Abstract :Particle Swarm Optimization insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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.
Keywords : K Ortalamalar, PSO, Yoğunlaştırma stratejisi