基于pso的CPSO参数调整方法

I. Ziari, A. Jalilian
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引用次数: 1

摘要

本文提出了一种改进的粒子群优化算法(MPSO),该算法具有传统粒子群优化算法的所有显著优点,并且具有较低的捕获早收敛的可能性和较高的精度。本文首先研究了CPSO参数变化对输出精度的影响;然后,研究了一种改进的粒子群算法(MPSO),对这些参数进行最优计算,改善了粒子群算法的早熟收敛问题和精度。在所提出的方法中,使用另一种典型参数选择的CPSO算法确定CPSO参数。为了评估所提出的MPSO,考虑了一个6母线电力系统,其中两个非线性负载被定位为谐波发生器。通过将MPSO算法与遗传算法(GA)和CPSO算法的结果进行比较,证明了基于MPSO算法的适用性和有效性,以及其相对于其他技术的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A PSO-based approach to adjust CPSO parameters
This paper introduces a modified particle swarm optimization (MPSO) algorithm which gets benefit from all remarkable advantages of conventional PSO (CPSO) in addition to lower possibility of catching in premature convergence and higher accuracy. In this paper, influence of CPSO parameters changes on the output accuracy is firstly represented and studied; then, a modified PSO called MPSO is studied to calculate these parameters optimally and improve the premature convergence problem along with the accuracy. In the proposed approach, CPSO parameters are determined using another CPSO algorithm in which parameters are selected typically. To evaluate the proposed MPSO, a 6-bus power system is considered in which two nonlinear loads are located as harmonics generators. A Comparison between the results of MPSO and those of CPSO and genetic algorithm (GA) is used to demonstrate the applicability and effectiveness of the MPSO-based algorithm and its superiority over other techniques.
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