A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints

Particle swarm optimization (PSO) is inspired by social behaviors of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses, and both of them are pertain to meta-heuristic algorithms. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), having attributes of PSO and GSA, is proposed in this paper to solve economic emission load dispatch (EELD) problems considering various practical constraints. These constraints consist of the generator ramp rate limits, non-convex and discontinuous nature of prohibited operating zones, non-smooth characteristic of valve-point effects, multiple fuels type of generation units, and transmission losses in realistic power systems. The proposed approach embodies interesting concepts and fully incorporates the social essence of PSO with the motion mechanism of GSA. The proposed HPSO-GSA adopts co-evolutionary technique to simultaneously update particle positions with PSO velocity and GSA acceleration. HPSO-GSA, therefore, is expected to obtain an efficient balance between exploration and exploitation. From results of canonical benchmark test functions, HPSO-GSA does significantly improve PSO and GSA with better performance. As a real application, the EELD problems on five test systems including different constraints are solved by the HPSO-GSA to assess the optimizing performance of the proposed hybrid approach. The results obtained confirm the potential and effectiveness of the proposed approach compared to PSO, GSA and other algorithms published in the recent state-of-the art literatures for the solution of the EELD problems.