A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization

被引：27

作者：

Luo, Jianping ^{[1
,2
,3
]}

Yang, Yun ^{[1
,2
,3
]}

Liu, Qiqi ^{[1
,2
,3
]}

Li, Xia ^{[1
,2
,3
]}

Chen, Minrong ^{[1
,2
,3
]}

Gao, Kaizhou ^{[1
,2
,3
]}

机构：

[1] Guangdong Key Lab Intelligent Informat Proc, Shenzhen, Peoples R China

[2] Shenzhen Key Lab Media Secur, Shenzhen, Peoples R China

[3] Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China

来源：

INFORMATION SCIENCES | 2018年 / 448卷

基金：

中国国家自然科学基金;

关键词：

Evolutionary computing; Hybrid computing; Multiple objective programming; Algorithm diversity; Shuffled frog leaping algorithm; Extremal optimization; NONDOMINATED SORTING APPROACH; FROG LEAPING ALGORITHM; EVOLUTIONARY ALGORITHM; PERFORMANCE; COMPUTATION; MOEA/D; MODEL; TIME;

D O I：

10.1016/j.ins.2018.03.012

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To deal with the multi-objective optimization problems (MOPS), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail. (C) 2018 Elsevier Inc. All rights reserved.

引用

页码：164 / 186

页数：23

共 47 条

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