Abstract
Many-objective optimization problems are common in real-world applications, few evolutionary optimization methods, however, are suitable for them up to date due to their difficulties. We proposed a reference points-based evolutionary algorithm (RPEA) to solve many-objective optimization problems in this study. In RPEA, a series of reference points with good performances in convergence and distribution are generated according to the current population to guide the evolution. Furthermore, superior individuals are selected based on the assessment of each individual by calculating the distances between the reference points and the individual in the objective space. The algorithm was applied to four benchmark optimization problems and compared with NSGA-II and HypE. The results experimentally demonstrate that the algorithm is strengthened in obtaining Pareto optimal set with high performances.
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