Convergence analysis of particle swarm optimization algorithms for different constriction factors - Tarekegn Nigatu, Dereje; Gemechu Dinka, Tekle; Luleseged Tilahun, Surafel (Frontiers in Applied Mathematics and Statistics, 2024)
Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism of controlling the velocity applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of CSPSO algorithm based on constriction coefficient with some benchmark functions, and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.