Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition

被引：15

作者：

Chen, Liang ^{[1
,2
]}

Gan, Wenyan ^{[1
]}

Li, Hongwei ^{[1
]}

Cheng, Kai ^{[1
]}

Pan, Darong ^{[3
]}

Chen, Li ^{[3
]}

Zhang, Zili ^{[2
]}

机构：

[1] 1 Haifu Lane,Guanghua Rd, Nanjing 1, Jiangsu, Peoples R China

[2] 1155 Yanshan Rd, Bengbu City, Anhui, Peoples R China

[3] 1 Hongjing Ave,Jiangning Sci Pk, Nanjing, Jiangsu, Peoples R China

来源：

APPLIED INTELLIGENCE | 2021年 / 51卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Cuckoo search; Multi-objective; Decomposition; Angle-based selection; Adaptive operator selection; ANT COLONY OPTIMIZATION; EVOLUTIONARY ALGORITHM; BALANCING CONVERGENCE; OPERATOR; MOEA/D; PERFORMANCE; STRATEGIES; DIVERSITY; CROSSOVER; SELECTION;

D O I：

10.1007/s10489-020-01816-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, cuckoo search (CS) algorithm has been successfully applied in single-objective optimization problems. In addition, decomposition-based multi-objective evolutionary algorithms (MOEA/D) have high performance for multi-objective optimization problems (MOPs). Inspired by this, a new decomposition-based multi-objective CS algorithm is proposed in this paper. Two reproduction operators with different characteristics derived from the CS algorithm are constructed and they compose an operator pool. Then, a bandit-based adaptive operator selection method is used to determine the application of different operators. An angle-based selection strategy that achieves a better balance between convergence and diversity is adopted to preserve diversity. Compared with other improved strategies designed for MOEA/D on two suits of test instances, the proposed algorithm was demonstrated to be effective and competitive for MOPs.

引用

页码：143 / 160

页数：18

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