Associate Professor Tsung-Che Chiang

Department of Computer Science and Information Engineering
National Taiwan Normal University

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Tsung-Che Chiang and Yung-Pin Lai, MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism, Proc. of IEEE Congress on Evolutionary Computation, pp. 1473 - 1480, New Orleans, June, 2011.

Full text: Official version

Abstract

In this paper we propose a multiobjective evolutionary algorithm based on MOEA/D for solving multiobjective optimization problems. MOEA/D decomposes a multiobjective optimization problem into many single-objective subproblems. The objective of each subproblem is a weighted aggregation of the original objectives. Using evenly distributed weight vectors on subproblems, solutions to subproblems form a set of well-spread approximated Pareto optimal solutions to the original problem. In MOEA/D, each individual in the population represents the current best solution to one subproblem. Mating selection is carried out in a uniform and static manner. Each individual/subproblem is selected/solved once at each generation, and the mating pool of each individual is determined and fixed based on the distance between weight vectors on the objective space. We propose an adaptive mating selection mechanism for MOEA/D. It classifies subproblems into solved ones and unsolved ones and selects only individuals of unsolved subproblems. Besides, it dynamically adjusts the mating pools of individuals according to their distance on the decision space. The proposed algorithm, MOEA/D-AMS, is compared with two versions of MOEA/D using nine continuous functions. The experimental results confirm the benefits of the adaptive mating selection mechanism.

Benchmark problem instances

• H. Li and Q. Zhang, "Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284-302, 2009.

Benchmark algorithms

• H. Li and Q. Zhang, "Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284 - 302, 2009.

• Q. Zhang, W. Liu, and H. Li, “The performance of a new version of MOEA/D on CEC09 Unconstrained MOP test instances,” Proc. of IEEE Congress on Evolutionary Computation, pp. 203 - 208, 2009.

List of non-dominated solutions

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