A Gene-Level Hybrid Crossover Operator for Multiobjective Evolutionary Algorithm

被引:4
|
作者
Zhu, Qingling [1 ]
Lin, Qiuzhen [1 ]
Chen, Jianyong [1 ]
Huang, Peizhi [1 ]
机构
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
来源
2015 SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND MACHINE INTELLIGENCE (ISCMI) | 2015年
关键词
evolutionary algorithm; simulated binary crossover; differential evolution; hybrid crossover; OPTIMIZATION;
D O I
10.1109/ISCMI.2015.25
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study proposes a novel recombination operator, called hybrid crossover operator (HX), which is performed in gene level of chromosome to enhance the optimization performance of multi-objective evolutionary algorithms (MOEAs). The proposed HX operator combines the advantages of simulated binary crossover with local search ability and differential evolution with strong global search capability. When HX is embedded into two state-of-the-art MOEAs, i.e., NSGA-II and MOEA/D-DE, the experimental results validate the improvement of HX when compared to the original counterpart.
引用
收藏
页码:20 / 24
页数:5
相关论文
共 50 条
  • [21] A dual-operator strategy for a multiobjective evolutionary algorithm based on decomposition
    Yan, Zeyuan
    Tan, Yanyan
    Wang, Bin
    Liu, Li
    Zhang, Huaxiang
    Knowledge-Based Systems, 2022, 240
  • [22] Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator
    Zhang, Tao
    Hu, Tiesong
    Guo, Xuning
    Chen, Zhong
    Zheng, Yue
    KNOWLEDGE-BASED SYSTEMS, 2013, 53 : 13 - 19
  • [23] A Parametric Study of Crossover Operators in Pareto-Based Multiobjective Evolutionary Algorithm
    Maruyama, Shohei
    Tatsukawa, Tomoaki
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2017, PT II, 2017, 10386 : 3 - 14
  • [24] An hybrid evolutionary multiobjective algorithm for multiuser margin maximization in DSL
    Gomes, Ana
    Monteiro, Marcio
    Dortschy, Boris
    Klautau, Aldebaro
    INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2016, 29 (01) : 194 - 209
  • [25] Multiobjective Evolutionary Algorithm Based on Hybrid Individual Selection Mechanism
    Chen X.-J.
    Shi C.
    Zhou A.-M.
    Wu B.
    Ruan Jian Xue Bao/Journal of Software, 2019, 30 (12): : 3651 - 3664
  • [26] Hybrid Sampling Strategy-based Multiobjective Evolutionary Algorithm
    Zhang, Wenqiang
    Lin, Lin
    Gen, Mitsuo
    Chien, Chen-Fu
    COMPLEX ADAPTIVE SYSTEMS 2012, 2012, 12 : 96 - 101
  • [27] A hybrid multiobjective evolutionary algorithm: Striking a balance with local search
    Ahn, Chang Wook
    Kim, Eungyeong
    Kim, Hyun-Tae
    Lim, Dong-Hyun
    An, Jinung
    MATHEMATICAL AND COMPUTER MODELLING, 2010, 52 (11-12) : 2048 - 2059
  • [28] Multiobjective optimal power flow using hybrid evolutionary Algorithm
    Alawode Kehinde, O.
    Jubril Abimbola, M.
    Komolafe Olusola, A.
    World Academy of Science, Engineering and Technology, 2009, 39 : 790 - 795
  • [29] Multiplex PCR assay design by hybrid multiobjective evolutionary algorithm
    Lee, In-Hee
    Shin, Soo-Yong
    Zhang, Byoung-Tak
    EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS, 2007, 4403 : 376 - +
  • [30] A multiobjective hybrid evolutionary algorithm for robust design of distribution networks
    Carrano, Eduardo G.
    Taroco, Cristiane G.
    Neto, Oriane M.
    Takahashi, Ricardo H. C.
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 63 : 645 - 656