【摘要】 通过把Pareto优与粒子群优化(PSO)算法相结合,利用给出的粒子的序值定义对粒子群中的粒子进行分离存档,给出了一种求解多目标优化问题的新粒子群存档算法。为了提高算法的全局收敛性,对PSO算法中的惯性因子ω执行自适应调节。数据实验比较表明该算法能找到问题数量更多、分布更广、更均匀的Pareto最优解。更多还原

【Abstract】 By the combination of pareto optimization and particle swarm optimization(PSO) algorithm,a new particle swarm achieve algorithm for multi-objective optimization is proposed,in which the particles are put into an achieve according to the definition of the rank of the particle.In ordering to improve its global convergence,the inertia weight of PSO algorithm has adjusted automatically.The numerical experiment shows that this algorithm can find more and wider Pareto-optimal solutions than the original one.更多还原