Adaptive Weighted Hybrid PSOGSA Algorithm and its Application in Power System Economic Load Dispatch Problem

Abstract

Gravity Search Algorithm (GSA) is a swarm intelligence optimization algorithm based on the gravity law. The standard GSA algorithm has strong global search capability, while its convergence speed is slow. The Particle Swarm Optimization (PSO) algorithm has high convergence speed and search efficiency. Based on the advantages of the above two algorithms, a hybrid algorithm(PSOGSA) is proposed in this paper, and two adaptive weighted update strategies are introduced into the optimization process to improve the search accuracy of the hybrid algorithm. At the same time, we added variable mutation probability to solve the problem that particles are easily be trapped in local optimum. In order to verify the effectiveness of the two improved hybrid algorithms, the two algorithms are applied to the power system economic load dispatch (ELD) problem. Power generation cost optimization performance tests are computed for three groups of power systems with different unit numbers. The simulation results show that the two adaptive weighted hybrid algorithms which are proposed in this paper can effectively reduce the generation cost of the power system.