Analysis of mutation vectors selection mechanism in differential evolution

被引:6
作者
Zhou, Yinzhi [1 ]
Yi, Wenchao [1 ]
Gao, Liang [1 ]
Li, Xinyu [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
关键词
Differential evolution; Probability density function; Mutation operation; CONTROL PARAMETERS; OPTIMIZATION; ALGORITHM;
D O I
10.1007/s10489-015-0738-y
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), which is one of the most popular evolution algorithms, has received much attention from researchers and engineers. In DE, mutation operation has a great impact on the performance of the algorithm. It generates mutant vectors by adding difference vectors to the base vector. Obviously, the chosen vectors in the mutation operation should not be equal to each other or to the target vector. This paper designs four experiments to analyze this session of DE and tries to determine whether this constraint is necessary. The theoretical analysis and experimental results show that without this constraint, the DE algorithm may perform better or at least not worse. Moreover, based on the experimental results, we can also summarize some rules for when and how to use this constraint to enhance the performance of the DE algorithm. This can help researchers to improve or apply the DE algorithm well.
引用
收藏
页码:904 / 912
页数:9
相关论文
共 21 条
[1]   Differential evolution with preferential crossover [J].
Ali, M. M. .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2007, 181 (03) :1137-1147
[2]   An efficient Differential Evolution based algorithm for solving multi-objective optimization problems [J].
Ali, Musrrat. ;
Siarry, Patrick ;
Pant, Millie. .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2012, 217 (02) :404-416
[3]   Purposeful model parameters genesis in simple genetic algorithms [J].
Angelova, Maria ;
Atanassov, Krassimir ;
Pencheva, Tania .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2012, 64 (03) :221-228
[4]  
[Anonymous], 2005, PROBLEM DEFINITIONS
[5]   An adaptive hybrid differential evolution algorithm for single objective optimization [J].
Asafuddoula, Md ;
Ray, Tapabrata ;
Sarker, Ruhul .
APPLIED MATHEMATICS AND COMPUTATION, 2014, 231 :601-618
[6]   Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems [J].
Brest, Janez ;
Greiner, Saso ;
Boskovic, Borko ;
Mernik, Marjan ;
Zumer, Vijern .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (06) :646-657
[7]   Optimizing the positive Lyapunov exponent in multi-scroll chaotic oscillators with differential evolution algorithm [J].
Carbajal-Gomez, V. H. ;
Tlelo-Cuautle, E. ;
Fernandez, F. V. .
APPLIED MATHEMATICS AND COMPUTATION, 2013, 219 (15) :8163-8168
[8]  
Deb K., 1994, Annals of Mathematics and Artificial Intelligence, V10, P385, DOI 10.1007/BF01531277
[9]  
Gamperle R., 2002, ADV INTELLIGENT SYST, V10, P293
[10]   Multi-objective optimization based reverse strategy with differential evolution algorithm for constrained optimization problems [J].
Gao, Liang ;
Zhou, Yinzhi ;
Li, Xinyu ;
Pan, Quanke ;
Yi, Wenchao .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (14) :5976-5987