A MOEA/D-based multi-objective optimization algorithm for remote medical

作者：Lin, Shufu; Lin, Fan^*; Chen, Haishan; Zeng, Wenhua

来源：Neurocomputing, 2017, 220: 5-16.

DOI：10.1016/j.neucom.2016.01.124

• Remote medical
• Resource assignment
• Differential mutation
• Selection strategy
• Multi-objective optimization
• Test problems

Remote medical resources configuration and management involves complex combinatorial Multi-Objective Optimization problem, whose computational complexity is a typical NP problem. Based on the MOEA/D framework, this paper applies the two-way local search strategy and the new selection strategy based on domination amount and proposes the IMOEA/D framework, following which each individual produces two individuals in mutation. In this paper, by using a new selection strategy, the parent individual is compared with two mutated offspring individuals, and the more excellent one is selected for the next generation of evolution. The proposed algorithm IMOEA/D is compared with eMOEA, MOEA/D and NSGA-II, and experimental results show that for most test functions, IMOEA/D proposed is superior to the other three algorithms in terms of convergence rate and distribution.

• 出版日期2017-1-12
• 单位厦门大学