Multi-objective particle swarm optimization based on decision preferences and its application

To overcome shortcomings of traditional multi-objective particle swarm methods in dealing with complicated multi-objective optimization problems, an interactive Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was presented based on decision preferences. This algorithm considered the guiding roles for particles by the bipolar preferences of decision makers. The nearness degrees of out-archives particles to bipolar preferences were calculated and sequenced according to Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. Then, out-archives management and global-best solution update were implemented according to the sorting results, and the spreading of Pareto solution set was controlled by the-neighborhood. By applying the algorithm in stochastic multiple-target inventory control, it was proved to be effective in dealing with complicated application problems. Also, the comparison result showed that this algorithm outperformed Reference Non-dominated Sorting Genetic Algorithms II(R-NSGA-II) in convergence, diversity, and computing time.