T-MOEA/D: MOEA/D with Objective Transform in Multi-objective Problems
To approximate the Pareto optimal solutions of a multi-objective optimization problem, Zhang and Li 8 have recently developed a novel multi-objective evolutionary algorithm based on decomposition (MOEA/D). It can work well if the curve shape of the Pareto-optimal front is friendly. Otherwise, it might fail. In this paper, we propose an unproved MOEA/D algorithm (denoted as TMOEA/D), which utilizes a monotonic increasing function to transform each individual objective function into the one so that the curve shape of the non-dominant solutions of the transformed multi-objective problem is close to the hyper-plane whose intercept of coordinate axes is equal to one in the original objective function space. Consequently, we can approximate the Pareto optimal solutions that are uniformly distributed over the Pareto front using the advanced decomposition technique of MOEA/D. Numerical results show that the proposed algorithm has a good performance.
Multi-objective optimization evolutionary algorithm uniformly distribution Pareto front
Hai-Lin Liu Fang-qing Gu Yiu-ming Cheung
Department of Applied Mathematics, Guangdong University of Technology, China Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
国际会议
西安
英文
862-865
2010-08-07（万方平台首次上网日期，不代表论文的发表时间）