摘要
为了降低分解型算法求解大规模问题的运行时间成本,结合分解型多目标进化算法(MOEA/D)和Spark分布式计算框架的特点,提出了一个主从分布式分解型多目标进化算法(MODEA/D-RDD)。在新的方案中每个Map保存且进化一个子问题,从而通过多个Map分布式计算提高效率。测试例上的实验结果表明,在求得解集质量不明显降低的前提下,全局种群进化方案能够有效缩短求解多目标问题的计算时间。
In order to reduce the running time cost of decomposition algorithm for solving large-scale problems, a master-slave distributed multi-objective evolutionary algorithm(MODEA/D-RDD) is proposed based on the characteristics of the decomposition multiobjective evolutionary algorithm(MOEA/D) and Spark distributed computing framework. In the new scheme, each Map saves and evolves a sub problem, so as to improve the efficiency through multiple Map distributed computing. The Experimental results on test cases show that the global population evolution scheme can effectively shorten the computational time on solving multi-objective problems on the premise that the quality of the solution set is not significantly reduced.
作者
何昱琪
李德禹
HE Yuqi;LI Deyu(Jinan University–University of Birmingham Joint Institute,Guangzhou 511443,China)
出处
《现代信息科技》
2021年第22期66-70,共5页
Modern Information Technology
