A Novel Hybrid PSO-GSA Method for Non-convex Economic Dispatch Problems

Abstract

Abstract-This paper proposes a novel and efficient hybrid algorithm based on combining particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques, called PSO-GSA. The core of this algorithm is to combine the ability of social thinking in PSO with the local search capability of GSA. Many practical constraints of generators, such as power loss, ramp rate limits, prohibited operating zones and valve point effect, are considered. The new algorithm is implemented to the non-convex economic dispatch (ED) problem so as to minimize the total generation cost when considering the linear and non linear constraints. In order to validate of the proposed algorithm, it is applied to two cases with six and thirteen generators, respectively. The results show that the proposed algorithms indeed produce more optimal solution in both cases when compared results of other optimization algorithms reported in literature.

Single and Multi-Objective Optimization Algorithms

“…Hybrid algorithms are a combination between two or more algorithms such as hybrid Particle Swarm Optimization with Gravitational Search Algorithm (PSOGSA) [22,23], and Particle Swarm Optimization with Dragonfly Algorithm (PSODA) [24], and Particle Swarm Optimization with Firefly Algorithm (PSOFA) [25], and Particle Swarm Optimization with Multi Verse Optimizer (PSOMVO) [26].…”