This paper presents an enhanced particle swarm optimization (PSO) algorithm, which applied to reactive power compensation (RPC) problem. It integrates the merits of both GAs and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which traveling through the search space, during this travel; an evolution of these particles is performed by a PSO coupled with GA to get approximate nondominated solutions. 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. Also In order to study the algorithm performance, the effect of change of the most significant parameters of the proposed approach was studied. 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. Also the results declare that it is quite difficult to find fixed
values for these significant parameters, thus we recommend to develop dynamic version of the proposed approach using any monitoring algorithm.