Directed mating in decomposition-based MOEA for constrained many-objective optimization
Minami Miyakawa, Hiroyuki Satō, Yuji Sato · Proceedings of the Genetic and Evolutionary Computation Conference · 2018
This work proposes a decomposition-based algorithm, CMOEA/D-DMA, for constrained many-objective optimization. For constrained multi-objective optimization, the TNSDM algorithm using the directed mating selecting infeasible solutions having better objective values than feasible ones as parents was proposed and verified its effectiveness on several test problems. However, since TNSDM uses the non-dominated sorting, its search performance deteriorates when the number of objectives is increased. For many-objective optimization, the decomposing objective space is a promising approach, and MOEA/D is known as its representative algorithm. However, since the conventional MOEA/D maintains only one solution for each weight vector, feasible solutions are preferred rather than infeasible ones. Infeasible solutions are just discarded even if they have better objective values than maintained feasible solutions and can provide clues for the search. For solving constrained many-objective optimization problems, in this work, we propose CMOEA/D-DMA combining MOEA/D with the Directed Mating and Archives of infeasible solutions. The directed mating in CMOEA/D-DMA selects useful infeasible solutions having better scalarizing function values than feasible ones as parents and maintains them in archives. The experimental results using continuous mCDTLZ and discrete knapsack problems with many-objectives show that the proposed CMOEA/D-DMA achieves higher search performance than the conventional TNSDM, MOEA/D, and NSGA-III.