The performance of traditional multiobjective evolutionary algorithms (MOEAs) often deteriorates rapidly as the number of decision variables increases. While some efforts were made to design new algorithms by adapting existing techniques to large-scale single-objective optimization to the MOEA context, the specific difficulties that may arise from large-scale multiobjective optimization have rarely been studied. In this paper, the exclusive challenges along with the increase of the number of variables of a multiobjective optimization problem (MOP) are examined empirically, and the popular benchmarks are categorized into three groups accordingly. Problems in the first category only require MOEAs to have stronger convergence, and can thus be mitigated using techniques employed in large-scale single-objective optimization. Problems that require MOEAs to have stronger diversification but ignore a correlation between position and distance functions are grouped as the second. The rest of the problems that pose a great challenge to the balance between diversification and convergence by considering a correlation between position and distance functions are grouped as the third. While existing large-scale MOEAs perform well on the problems in the first two categories, they suffer a significant loss when applied to those in the third category. To solve large-scale MOPs in this category, we have developed a novel indicator-based algorithm with an enhanced diversification mechanism. The proposed algorithm incorporates a new solution generator with an external archive, thus forcing the search toward different subregions of the Pareto front using a dual local search mechanism. The results obtained by applying the proposed algorithm to a wide variety of problems (108 instances in total) with up to 8192 variables demonstrate that it outperforms eight state-of-the-art approaches on the examined problems in the third category and show its advantage in the balance between diversification and convergence. © 1997-2012 IEEE.
Bibliographical noteThis work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1003102, in part by the Natural Science Foundation of China under Grant 61672478, in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008, in part by the Royal Society Newton Advanced Fellowship under Grant NA150123, and in part by the IEEE Computational Intelligence Society Graduate Student Research Grant 2018.
- indicator-based evolutionary algorithm (EA)
- large-scale optimization
- multiobjective optimization