Abstract—In this paper a new evolutionary learning algorithm based on a hybrid of improved real-code genetic algorithm (IGA) and particle swarm optimization (PSO) called HIGAPSO is proposed to solve the optimal reactive power dispatch (ORPD) Problem. In order to overcome the drawbacks of standard genetic algorithm and particle swarm optimization, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance, which are elitist strategy, PSO strategy and improved genetic algorithm (IGA) strategy. The effectiveness of the proposed algorithm has been compared with Gas and PSO, synthesizing a circular array, a linear array and a base station array. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system consisting 6 generator and compared other algorithms and simulation results show that HIGAPSO is more efficient than others for solution of single-objective ORPD problem.

Index Terms—particle swarm , improved genetic algorithm, function optimization, optimal reactive Power dispatch, power system

Cite: K. Lenin, B. Ravindranath Reddy, and M. Surya Kalavathi, "A New Improved GA and PSO Combined Hybrid Algorithm (HIGAPSO) for Solving Optimal Reactive Power Dispatch Problem," Journal of Industrial and Intelligent Information, Vol. 2, No. 3, pp. 205-209, September 2014. doi: 10.12720/jiii.2.3.205-209