Improvement of Particle Swarm Optimization Focusing on Diversity of the Particle Swarm

PSO (Particle Swarm Optimization) is attracting attention in recent years to solve the multivariate optimization problems. In PSO, multiple individuals (particles) which records its own position and velocity information are placed in the corresponding search space, and the particle swarm move to discover the optimal solution by sharing information with other particles. The search process of PSO has problem such that it is difficult to deviate from the local solution because of convergence speed of the swarms is too fast. In TCPSO (Two-Swarm Cooperative PSO), particle swarm consists of two different types of particles (a master particle swarm and a slave particle swarm) with different characteristics of search process. Experimental results of using several benchmark problems indicate that TCPSO has high performance of finding optimal solutions for multidimensional and nonlinear problems. This study introduces the concept of specificity of each master particle which indicates the diversity of master particle swarm, and proposes an algorithm that improves the efficiency of the solution search process in TCPSO by periodically analyzing the behavior of master particle swarm. This study conducts several numerical experiments for verifying the effectiveness of the proposed method.