关键词: Powell搜索法, 微粒群算法, 无约束最优化

Abstract: The hybrid Powell-LLPSO algorithm based on the Powell search method and the local constriction approach particle swarm optimization with linear varying inertia weight for unconstrained optimization was proposed. Powel-LLPSO is very easy to implement in practice since does not require gradient computation. The modification of both the Powell search method and particle swarm optimization intends to produce faster and more accurate convergence. The main purpose is to demonstrate how the standard particle swarm optimizers can be improved by incorporating a hybrid strategy. In a suit of 20 test function problems taken from the literature, computational results via a comprehensive experimental study, preceded by the investigation of parameter selection, show that the hybrid Powell-LLPSO approach outperforms other three relevant search techniques (the original PSO, the guaranteed convergence particle swarm optimization (GCPSO) and hybrid NM-PSO) in terms of solution quality and convergence rate. In a later part of the comparative experiment, the Powell-LLPSO algorithm was compared to various most up-to-date cooperative PSO (CPSO) procedures appearing in the literature. The comparison report still largely favors the Powell-LLPSO algorithm in the performance of accuracy, robustness and function evaluation.

Key words: Powell search method, Particle swarm method, Unconstrained optimization