A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch

In this paper, we propose a new bare-bones multi-objective particle swarm optimization algorithm to solve the environmental/economic dispatch problems. The algorithm has three distinctive features: a particle updating strategy which does not require tuning up control parameters; a mutation operator with action range varying over time to expand the search capability; and an approach based on particle diversity to update the global particle leaders. Several trials have been carried out on the IEEE 30-bus test system. By comparing with seven existing multi-objective optimization algorithms and three well-known multi-objective particle swarm optimization techniques, it is found that our algorithm is capable of generating excellent approximation of the true Pareto front and can be used to solve other types of multi-objective optimization problems.

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