Improved Multi-objective PSO for Semi-desirable Facility Location Problem
نویسندگان
چکیده
Evolutionary optimization algorithms have been used to solve multiple objective problems. However, most of these methods have focused on search a sufficient Pareto front, and no efforts are made to explore the diverse Pareto optimal solutions corresponding to a Pareto front. Note that in semi-obnoxious facility location problems, diversifying Pareto optimal solutions is important. The paper therefore suggests an improved multi-objective particle swarm optimization algorithm (MOPSO) to find diversified Pareto optimal solutions in the parameter space for semi-obnoxious facility location problems while achieving a similar Pareto front in the objective space. The improvement of MOPSO is obtained by introducing a new mechanism based on distances among Pareto optimal solutions. Three semiobnoxious facility location problems from the literature are used to evaluate the performance of the improved MOPSO. The results indicate that the approach is promising, being able to expand the diversity of non-dominated/Pareto Optimal solutions while acquiring a similar Pareto front.
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