Hybrid of comprehensive learning particle swarm optimization and SQP algorithm for large scale economic load dispatch optimization of power system

Economic load dispatch (ELD) is an important optimization task in power systems. In the previous works, various researchers attempted to address this problem by both mathmatical and heuristic optimization algorithms. However, there are still two practically important issues that have not attracted sufficient attention: 1) the stability of these algorithms cannot be effectively ensured; 2) the performance of these algorithms on large scale ELD optimization tasks remains to be unsatisfactory. CLPSO is an effective global optimization algorithm. To strengthen the convergence ability of CLPSO, the sequential quadratic programming (SQP) is introduced into it. This results in a new algorithm hybrid of comprehensive learning particle swarm optimization and sequential quadratic programming (SQP-CLPSO). To assess the performance of SQP-CLPSO, it is compared with several state-of-the-art evolutionary algorithms (EAs) on the classical ELD optimization problems. Experimental results show that SQP-CLPSO has very good abilities of convergence, diversity maintainence and scalability, which make it suitable for complex ELD problems.