关键词: 粒子群算法, 进食过程, 二次飞行, 共轭梯度, 混合优化

Abstract: Particle Swarm Optimization (PSO) is an intelligent evolutionary approach widely used to search for the global optimal solution. However, fast flying of swarm particles to the current optimal solution at the early algorithm phase may result in premature convergence, and at the late phase, convergence of a majority of particles causes the degradation of swarm speed. To deal with those shortcomings, a new global algorithm named Eating Particle Swarm Optimization (EPSO) was put forward. In this algorithm, the concepts of eating process and second flight were introduced to guarantee particles flying quickly away from the current optimal solution, so that individual diversity was enhanced. Then the proposed EPSO was combined with Conjugate Gradient (CG) method to form a mixed optimization strategy, in which CG was applied to the local optimization of EPSO algorithm to improve the convergence speed and precision. High-dimensional Benchmark functions were used for optimization experiments, of which the results were compared with the methods in recent literature. The results show that the proposed approach can avoid local optimal phenomena, and obtains the merits of CG in terms of optimization accuracy and convergence speed.

Key words: Particle Swam Optimization (PSO), eating process, second flight, Conjugate Gradient (CG), mixed optimization