关键词: Hook-Jeeves搜索法 , 微粒群算法 , 无约束最优化

Abstract: The hybrid algorithm based on the Hook-Jeeves search method and the local constriction approach particle swarm optimization (PSO) with linear varying inertia weight (HJ-LLPSO) for unconstrained optimization was put forward. HJ-LLPSO is very easy to implement in practice since it does not require gradient computation. The modification of the 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 6 test function problems taken from the literature, computational results via a comprehensive experimental study show that the hybrid HJ-LLPSO approach outperforms other two relevant search techniques (i.e., the original PSO and PSO combined with chaos) in terms of solution quality and convergence rate. As evidenced by the overall assessment based on computational experience, the new algorithm is extremely effective and efficient at locating best-practice optimal solutions for unconstrained optimization.

Key words: Hook-Jeeves search method , Particle swarm optimization , Unconstrained optimization