GeesePSO: An Efficient Improvement to Particle Swarm Optimization
基于雁群启示的粒子群优化算法

Particle swarm optimization (PSO) is a new stochastic optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best solution of the whole swarm. In this paper, an improved algorithm is proposed using the characteristics of the flight of geese for reference. The improved algorithm has superiority over PSO; for one thing, it keeps the population various by ordering all the particles and making each particle fly following its anterior particle; for another thing, it strengthens cooperation and competition between particles by making each particle share more useful information of the other particles. Three benchmark functions are tested and the experimental results show that the new algorithm not only significantly speed up the convergence, but also effectively solve the premature convergence problem.