Modified particle swarm optimization using simplex search method for multiobjective economic emission dispatch problem

Namarta Chopra, Y. S. Brar, J. S. Dhillon
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引用次数: 3

Abstract

This paper presents the hybridization of particle swarm optimization (PSO) with simplex search method (SM). SM is a deterministic method which is generally used for the local search and PSO is a stochastic population based method used for global search. Thus, this paper combines the advantage of both the methods to refine the solution. Base level search is done using PSO and finally local search is done through SM to improve the overall quality of results obtained. The proposed approach is tested on multiobjective economic emission load dispatch problem and the results obtained are then compared with other available methods to show its quality and robustness. Price penalty factor method is further used to convert this multiobjective optimization problem into single objective problem. Multifuel options, valve point loading effect and transmission losses are also included in the load dispatch problem to give the practical aspect to the problem. Quality analysis is also done against classical PSO method to show the robustness of the proposed method.
基于单纯形搜索的修正粒子群算法求解多目标经济排放调度问题
提出了粒子群优化与单纯形搜索的混合算法。SM是一种确定性方法,通常用于局部搜索;粒子群算法是一种基于随机种群的方法,用于全局搜索。因此,本文结合了这两种方法的优点来细化解。采用粒子群算法进行基层搜索,最后通过SM进行局部搜索,提高搜索结果的整体质量。将该方法应用于多目标经济排放负荷调度问题,并与现有方法进行了比较,验证了该方法的有效性和鲁棒性。进一步利用价格惩罚因子法将多目标优化问题转化为单目标优化问题。在负荷调度问题中还考虑了多燃料选择、阀点加载效果和传输损失,使问题具有实用性。通过对经典粒子群算法的质量分析,验证了该方法的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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