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

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• 作者：Shufu LinAuthor Vitae ; Fan Lin ; ^{iamafan@xmu.edu.cn}Author Vitae ; Haishan ChenAuthor Vitae ; Wenhua ZengAuthor Vitae
• 关键词：Remote medical ; Resource assignment ; Differential mutation ; Selection strategy ; Multi-objective optimization ; Test problems
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：12 January 2017
• 年：2017
• 卷：220
• 期：Complete
• 页码：5-16
• 全文大小：2542 K
• 卷排序：220

文摘

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.