Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem

Abd Allah A. Mousa, Mohamed A. El-Shorbagy

Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shibin El Kom, Egypt.
Department of Mathematics and Statistics, Faculty of Sciences, Taif University, Taif, KSA.

DOI: 10.4236/am.2012.330184                

Abstract

This paper presents an enhanced Particle Swarm Optimization (PSO) algorithm applied to the reactive power compensation (RPC) problem. It is based on the combination of Genetic Algorithm (GA) and PSO. Our approach integrates the merits of both genetic algorithms (GAs) and particle swarm optimization (PSO) and it has two characteristic features. Firstly, the algorithm is initialized by a set of a random particle which traveling through the search space, during this travel an evolution of these particles is performed by a hybrid PSO with GA to get approximate no dominated solution. Secondly, to improve the solution quality, dynamic version of pattern search technique is implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective RPC.

Keywords

Multiobjective Optimization; Particle Swarm Optimization; Local Search

Conflicts of Interest

The authors declare no conflicts of interest.

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