A many-objective evolutionary algorithm based on rotation and decomposition
Evolutionary algorithms have shown their promise in addressing multiobjective problems (MOPs). However, the Pareto dominance used in multiobjective optimization loses its effectiveness when addressing many-objective problems (MaOPs), which are defined as having more than three objectives. This is because the Pareto dominance loses its ability to distinguish between individuals. In this paper, a many-objective evolutionary algorithm based on rotation and decomposition is proposed (MaOEA-RD) to overcome the shortcoming of insufficient selection pressure caused by the Pareto dominance. First, the coordinates system is rotated and a hyperplane is established to distinguish between the nondominated individuals. Then, a novel individual selection mechanism incorporating decomposition is adopted to maintain the diversity of the population. In order to compensate for the deficiency of the predefined reference vectors, a reference vector adjustment mechanism is proposed. Experimental studies on several well-known benchmark problems show that the proposed algorithm is competitive compared with seven state-of-the-art many-objective algorithms.
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Citation : Zou, J., Liu, J., Yang, S., and Zheng, J. (2021) A many-objective evolutionary algorithm based on rotation and decomposition. Swarm and Evolutionary Computation, 60, 100775.
ISSN : 2210-6502
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes