Decomposition of a Multiobjective Optimization Problem into a Number of Simple Multiobjective Subproblems

作者：Liu, Hai-Lin^*; Gu, Fangqing; Zhang, Qingfu

来源：IEEE Transactions on Evolutionary Computation, 2014, 18(3): 450-455.

DOI：10.1109/TEVC.2013.2281533

• Decomposition
• hybrid algorithms
• multiobjective optimization

This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better.

• 出版日期2014-6
• 单位广东工业大学