一种动态多点检测粒子群算法

Wang Yong, Pang Xing
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引用次数: 1

摘要

本文的主要目的是提出一种基于动态多点探索方法的粒子群优化算法。该算法的主要技术是在算法的前一阶段,每个粒子可以独立地选择自己的搜索方向和移动速度,而不受粒子群找到的最优位置的限制和吸引,采用动态多点随机检测方法。通过4个典型基准函数优化的实证结果表明,该优化算法具有收敛速度快、数值解精度高、稳定性好、鲁棒性强等特点。这证明了该算法是解决复杂函数优化问题的一种很有前途的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A dynamic multipoint detecting PSO
The chief aim of the present work is to propose a particle swarm optimization(PSO) by using a dynamic multipoint exploring approach. The main technique of this algorithm is that in the preceding phase of the algorithm, every particle can choose its searching direction and its moving velocity independently not being restricted or attracted by the optimal position of which have found by the parcle swarm and makes use of a dynamic multipoint random detecting method. It indicatess, from the empirical results of four typical benchmark functions' optimization, that the optimization algorithm has the performance of rapid convergence rate, high accurate numerical solution, good stability and powerful robust. This proves that the algorithm is a promising means in solving the complex function optimization problems.
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