Multi-objective particle swarm optimization with preference-based sorting

Abstract-Multi-objective particle swarm optimization (MOPSO) provides a set of nondominated solutions and the number of nondominated solutions increases exponentially when the number of objectives increases. To select a desired solution out of them, preference-based solution selection algorithm (PSSA) was proposed by incorporating user´s preference into multi-objective evolutionary algorithms. In this paper, multi objective particle swarm optimization with preference-based sorting (MOPSO-PS) is proposed, where a global best position is randomly selected from the archive of nondominated solutions sorted by global evaluation considering user´s preferences for multiple objectives. The user´s preference is represented as a degree of consideration for the objectives by the fuzzy measures. The global evaluation of the solutions is carried out by the fuzzy integral of partial evaluation with respect to the fuzzy measures, where the partial evaluation of each solution is obtained as a normalized objective function value. To demonstrate the effectiveness of the proposed MOPSO-PS, empirical comparisons to NSGA-II, MQEA, and MOPSO are carried out for the DTLZ functions. Experimental results show that the user´s preference is properly reflected in the selected solutions without any loss of overall quality and diversity.