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

摘要： 提出一个求解无约束最优化问题的新的混合算法Hooke-Jeeves搜索法和惯性权重线性调整的局部
收缩的微粒群算法的混合算法。该算法不需要计算梯度,容易应用于实际问题中。通过对微粒群算法的修正,使混
合算法具有更加精确和快速的收敛性。主要目的是通过加入混合策略证明标准微粒群算法是能够被改进的。利用
6个基准测试函数进行仿真计算比较,计算结果表明,新混合算法在求解质量和收敛速率上都优于其它的两种算法
(PSO 和与混沌相结合的PSO 算法)。仿真结果表明,新算法是求解无约束最优化问题的一个高效的算法。

关键词: Hook-Jeeves搜索法 , 微粒群算法 , 无约束最优化