Two local best based multi-objective particle swarm optimization algorithm to solve environmental/economic dispatch problem

A two local best based Multi-Objective Particle Swarm Optimization algorithm(2lb-MOPSO)is integrated with superiority of feasible solution constraint handling method in this paper to solve the nonlinear constrained multi-objective Environmental Economic Dispatch(EED) problem. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, the searching space is partitioned into fixed number of bins in the proposed algorithm. The algorithm uses two local best to lead the search particles which can increase the diversity of the population. The algorithm is combined with superiority of feasible solution constraint handling method and applied to the standard IEEE 30-bus six-generator test system. The performance is compared against several method obtained from the literature. The results show that the proposed algorithm is able to generate good performance in terms of both diversity and convergence in solving EED problems.