Multiobjective reactive power compensation with an ant colony optimization algorithm

This paper presents an ant colony optimization (ACO) algorithm applied to the reactive power compensation problem in a multiobjective context. The developed algorithm was denominated Electric Omicron (EO) given that it was inspired in the Omicron ACO proposed by some of the authors. The proposed EO algorithm was compared to a variant of the SPEA (strength Pareto evolutionary algorithm), specially designed for this problem. This variant of SPEA has previously shown an excellent performance in this type of problem. Experimental results presented in this paper show that the proposed EO outperforms SPEA, i.e., EO finds better Pareto solutions considering voltage deviation and investment. As long as we know, this is the first attempt to solve the reactive power compensation problem with an ACO algorithm in a multiobjective context.