出版社：The Japanese Society for Artificial Intelligence
摘要：In this work, we propose a novel multi-objective evolutionary algorithm (MOEA) which improves search performance of MOEA especially for many-objective combinatorial optimization problems. Pareto dominance based MOEAs such as NSGA-II and SPEA2 meet difficulty to rank solutions in the population noticeably deteriorating search performance as we increase the number of objectives. In the proposed method, we rank solutions by calculating Pareto partial dominance between solutions using r objective functions selected from m objective functions to induce appropriate selection pressure in many-objective optimization by Pareto-based MOEA. Also, we temporally switch r objective functions among mCr combinations in every interval generations Ig to optimize all of the objective functions throughout the entire evolution process. In this work, we use many-objective 0/1 knapsack problems to show the search performance of the proposed method and analyze its evolution behavior. Simulation results show that there is an optimum value for the number of objective functions r to be considered for the calculation of Pareto partial dominance and the interval (generation numbers) Ig to maximize the entire search performance. Also, the search performance of the proposed method is superior to recent state-of-the-art MOEAs, i.e., IBEA, CDAS and MSOPS. Furthermore, we show that the computational time of the proposed method is much less than IBEA, CDAS and MSOPS, and comparative or sometimes less than NSGA-II.
