Parallel and Distributed MOEA/D with Exclusively Evaluated Mating and Migration

被引：0

作者：

Sato, Yuji ^{[1
]}

Sato, Mikiko ^{[2
]}

Midtlyng, Mads ^{[3
]}

Miyakawa, Minami ^{[4
]}

机构：

[1] Hosei Univ, Fac Comp & Informat Sci, Tokyo, Japan

[2] Tokai Univ, Sch Informat & Telecommun Engn, Tokyo, Japan

[3] Hosei Univ, Grad Sch Comp & Informat Sci, Tokyo, Japan

[4] Sinshu Univ, Dept Elect Informat Syst Engn, Nagano, Japan

来源：

2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2020年

关键词：

MOEA/D; parallel and distributed processing; many-core environment; multi-objective evolutionary algorithms; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a method for many-core-based large-scale parallel and distributed computation of MOEA/D, a decomposition-based evolutionary multi-objective optimization algorithm. Standard parallel MOEA/D on many-core environments provides fast execution time, but uniformity and diversity of the Pareto front may be lost. To avoid this problem, we propose a method of defining a virtual overlapping zone between partitions and selecting individuals for mating and migration by evaluating individual populations in this area using weight vectors of adjacent partitions. Using a two-objective constrained knapsack problem for evaluation, we compare the proposed method with standard single-core execution, no-migration parallel MOEA/D, and parallel MOEA/D with standard migration, and show that the proposed method is effective in improving diversity in solution searching while shortening execution time and increasing the accuracy of solution searching.

引用

页数：8

共 15 条

[11]

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[12]

Sato Y, 2017, 2017 21ST ASIA PACIFIC SYMPOSIUM ON INTELLIGENT AND EVOLUTIONARY SYSTEMS (IES), P83, DOI 10.1109/IESYS.2017.8233566

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Zhang, Qingfu ;

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2007, 11 (06) :712-731

[14]

Zitzler E., 2001, Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, P95100

[15]

Zitzler E., 1999, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, V63

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