Comparison of multiobjective particle swarm optimization and evolutionary algorithms for optimal reactive power dispatch problem

The optimal reactive power dispatch (ORPD) problem is formulated as a complex multiobjective optimization problem, involving nonlinear functions, continuous and discrete variables and various constraints. Recently, multiobjective evolutionary algorithms (MOEAs) and multiobjective particle swarm optimization (MOPSO) have received a growing interest in solving the multiobjective optimization problems. In this paper, MOPSO, and two highly competitive algorithms of MOEAs, that is, nondominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm (SPEA2) are presented for solving the ORPD problem. Moreover, a mixed-variable handling method and an effective constraint handling approach are employed to deal with various types of variables and constraints. The proposed algorithms are evaluated on the standard IEEE 30-bus and 118-bus test systems. In addition, several multiobjective performance metrics are employed to compare these algorithms with respect to convergence, diversity, and computational efficiency. The results show the effectiveness of MOEAs and MOPSO for solving the ORPD problem. Furthermore, the comparison results indicate that MOPSO generally outperforms other algorithms for ORPD and has a great potential in dealing with large-scale optimal power flow problems.