A novel mutation differential evolution for global optimization

被引：21
|
作者：
Yu, Xiaobing [1 ,2 ,3 ]
Cai, Mei [2 ,3 ]
Cao, Jie [1 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing 210044, Jiangsu, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, China Inst Mfg Dev, Nanjing 210044, Jiangsu, Peoples R China
[3] Nanjing Univ Informat Sci & Technol, Sch Econ & Management, Nanjing 210044, Jiangsu, Peoples R China
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2015年 / 28卷 / 03期
关键词：
Evolutionary algorithm; global optimization; differential evolution; DE/best/2; particle swarm optimization; PARTICLE SWARM; PARAMETERS; ALGORITHM;
D O I：
10.3233/IFS-141388
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The success of DE in solving a specific problem crucially depends on appropriately choosing generation strategies and control parameter values. A novel mutation DE (MDE) is proposed to improve generation strategy DE/best/2. Adaptive mutation is conducted to current population when the population clusters around local optima. Control parameters are adapted based on constants. The performance of MDE is extensively evaluated on eighteen benchmark functions and compares favorably with the several DE variants.
引用
收藏
页码：1047 / 1060
页数：14
相关论文
共 50 条
  • [1] Differential Evolution with a Dimensional Mutation Strategy for Global Optimization
    Guan, Jing
    [J]. 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 2799 - 2804
  • [2] An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization
    Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India
    不详
    不详
    [J]. IEEE Trans Syst Man Cybern Part B Cybern, 2 (482-500):
  • [3] An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization
    Islam, Sk. Minhazul
    Das, Swagatam
    Ghosh, Saurav
    Roy, Subhrajit
    Suganthan, Ponnuthurai Nagaratnam
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2012, 42 (02): : 482 - 500
  • [4] An Adaptive Differential Evolution with Mutation Strategy Pools for Global Optimization
    Pang, Tingting
    Wei, Jing
    Chen, Kaige
    Wang, Zuling
    Sheng, Weiguo
    [J]. 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2022,
  • [5] A Modified Differential Evolution Algorithm with Cauchy Mutation for Global Optimization
    Ali, Musrrat
    Pant, Millie
    Singh, Ved Pal
    [J]. CONTEMPORARY COMPUTING, PROCEEDINGS, 2009, 40 : 127 - 137
  • [6] Differential Evolution Algorithm with Three Mutation Operators for Global Optimization
    Wang, Xuming
    Yu, Xiaobing
    [J]. MATHEMATICS, 2024, 12 (15)
  • [7] A novel hybrid adaptive differential evolution for global optimization
    Zhang, Zhiyong
    Zhu, Jianyong
    Nie, Feiping
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [8] Investigation of Mutation Strategies in Differential Evolution for Solving Global Optimization Problems
    Leon, Miguel
    Xiong, Ning
    [J]. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING ICAISC 2014, PT I, 2014, 8467 : 372 - 383
  • [9] Differential Evolution with Modified Mutation Strategy for Solving Global Optimization Problems
    Kumar, Pravesh
    Pant, Millie
    Singh, V. P.
    [J]. SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, PT I, 2011, 7076 : 11 - +
  • [10] An improved differential evolution algorithm with triangular mutation for global numerical optimization
    Mohamed, Ali Wagdy
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2015, 85 : 359 - 375
12345