Convergence analysis of particle swarm optimization algorithms for different constriction factors

Abstract

Read online

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 for controlling the velocity by 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 the 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.

Keywords

• PSO algorithms
• convergence and stability
• constriction factor
• Markov chain
• Monte Carlo