Improved MOCLPSO algorithm for environmental/economic dispatch

被引：6

作者：

Victoire, T. A. A. ^{[1
]}

Suganthan, P. N. ^{[1
]}

机构：

[1] Nanyang Technol Univ, Dept Elect & Elect Engn, Singapore 639798, Singapore

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/CEC.2007.4424863

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article proposes a Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) approach for multi-objective environmental/economic dispatch (EED) problem in electric power system. The EED problem is a non-linear constrained multi-objective optimization problem where the power generation cost and emission are treated as competing objectives. The proposed MOCLPSO approach handles the problem with competing and non-commensurable fuel cost and emission objectives and has a diversity-preserving mechanism using an external memory (called "repository") and Pareto dominance concept to find widely different Pareto-optimal solutions. Simulations are conducted on typical power system problems. The superiority of the algorithm in converging to the better Pareto optimal front with fewer fitness function evaluations is shown in general.

引用

页码：3072 / 3076

页数：5

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