改进型混沌粒子群算法求解函数均值问题

针对基本粒子群优化算法易陷入局部最优的缺陷,提出一种基于有限作用域的混沌粒子群优化算法。利用特定的初始分布涵盖全局最优值,利用混沌序列良好的非线性性质来影响粒子速度的更新过程;以有限作用域外的粒子遍历优化问题的可行域,从而增加粒子对可行域的广度搜索,以有限作用域内的粒子搜索最优值,从而提高全局最优值的精度搜索效率。把本文算法应用到函数均值求解的实验中,结果表明,本文算法具有较好的求解精度和求解效率值。

Novel chaotic particle swarm optimization algorithm and for solving function mean

The novel chaotic swarm particle swarm optimization algorithm was proposed based on limited effective region to deal with the problems of premature and local convergence of simple particle swarm optimization algorithm（PSO）.The special initialization was applied to cover the global optimization.The swarm diversities were increased by introducing the chaotic series nonlinear property in the velocity update process.The particles outside the limited effective region explored the feasible region to improve the exploration,and the particles in the limited effective region searched the optimization to improve the precision.The experiment result shows that the proposed algorithm has better accuracy and efficiency of solution.