A Comparison Between SPSO and QPSO from View Point of Optimization

Abstract

Particle swarm optimization (PSO) has magnetized different investigators who are concerned in dealing with different optimization problems, due to its ease of implementation and reasonable performance. However, PSO algorithm is trapped in the local optima easily because of the quick loss of the population variance. Hence, enhancement of the performance of PSO and detraction the relaying on factors are led to significant variants of SPSO. One important variant is the quantum behavior of particle swarm optimization (QPSO), which is dependent on the dynamical analysis of SPSO and quantum mechanics. This paper presents a notion for the optimization of nonlinear functions using swarm methodology and a comparison between SPSO and QPSO are given. These two algorithms are analyzed on both unimodal and multimodal, high and low dimensional continuous functions. The results on eight benchmark functions show that the QPSO algorithm can perform much better than the SPSO.