A Novel Particle Swarm Optimization Algorithm with Intelligent Weighting Mechanism

摘要

This paper presents a novel particle swarm optimization algorithm with an intelligent weighting mechanism, which is termed as weighted particle swarm optimization (WPSO) for short. The intelligent weighting mechanism is developed based on an effectiveness index to improve performance on a diverse set of problems and enhance the ability of local search infeasible region. Three search techniques, a non-uniform mutation operator, a differential mutation operator, and a local random search procedure are used to mutate the global best position and combined to get a further improved solution by using the weighted average. The performance of WPSO is tested on a set of well-known optimization benchmark functions and the optimization results are compared with four reported optimization methods in terms of solution quality and convergence speed. The experimental results demonstrate superior performance of the WPSO in solving optimization problems compared with other optimization methods.