Partitioned Parallelization of MOEA/D for Bi-objective Optimization on Clusters

被引：0
|
作者：
Xie, Yuehong [1 ]
Ying, Weiqin [1 ]
Wu, Yu [2 ]
Wu, Bingshen [1 ]
Chen, Shiyun [1 ]
He, Weipeng [1 ]
机构：
[1] S China Univ Technol, Sch Software Engn, Guangzhou 510006, Guangdong, Peoples R China
[2] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China
来源：
COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS, (ISICA 2015) | 2016年 / 575卷
关键词：
Evolutionary algorithm; Bi-objective optimization; Decomposition; Parallelization; Message-passing clusters; MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS;
D O I：
10.1007/978-981-10-0356-1_39
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has a remarkable overall performance for multi-objective optimization problems, but still consumes much time when solving complicated problems. A parallel MOEA/D (pMOEA/D) is proposed to solve bi-objective optimization problems on message-passing clusters more efficiently in this paper. The population is partitioned evenly over processors on a cluster by a partitioned island model. Besides, the sub-populations cooperate among separate processors on the cluster by the hybrid migration of both elitist individuals and utopian points. Experimental results on five bi-objective benchmark problems demonstrate that pMOEA/D achieves the satisfactory overall performance in terms of both speedup and quality of solutions on message-passing clusters.
引用
收藏
页码：373 / 381
页数：9
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