TY - JOUR
T1 - Differential evolution with dynamic combination based mutation operator and two-level parameter adaptation strategy
AU - Deng, Libao
AU - Li, Chunlei
AU - Lan, Yanfei
AU - Sun, Gaoji
AU - Shang, Changjing
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grants 71701187 , 62176075 and 71704162 , Natural Science Foundation of Shandong Province, China under Grant ZR2021MF063 , Fundamental Research Funds for the Central Universities, China under Grant HIT NSRIF 2019083 and Sr Cymru II COFUND Fellowship.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/4/15
Y1 - 2022/4/15
N2 - Differential evolution (DE) is a simple yet effective algorithm for numerical optimization, and its performance significantly depends on mutation operator and control parameters. Therefore, designing appropriate mutation operator and parameter regulation strategy is an important and necessary task. To improve the performance of DE algorithm, we propose a novel DE variant called DCDE based on a dynamic combination based mutation operator and a two-level parameter regulation strategy. More specifically, the newly proposed mutation operator contains a dynamic base vector that consists of two individuals, one is the current optimal individual while the other, called elite individual, is the best one among three randomly selected individuals, and they are dynamically combined by a weight parameter associated with the evolution process and the ranking status of the elite individual in the current population. Moreover, the scale factor and crossover rate in DCDE depend on the combined effect of a population-level parameter and one individual-level parameter, respectively. Both mutation operator and control parameters in DCDE are designed to achieve an appropriate balance between global exploration ability and local exploitation ability. To evaluate the performance of DCDE, comparison experiments are conducted with five state-of-the-art DE variants and three non-DE algorithms on solving 29 functions in IEEE CEC 2017 benchmark suite. The comparison results indicate that the proposed DCDE is significantly better than, or at least comparable to the adopted competitors.
AB - Differential evolution (DE) is a simple yet effective algorithm for numerical optimization, and its performance significantly depends on mutation operator and control parameters. Therefore, designing appropriate mutation operator and parameter regulation strategy is an important and necessary task. To improve the performance of DE algorithm, we propose a novel DE variant called DCDE based on a dynamic combination based mutation operator and a two-level parameter regulation strategy. More specifically, the newly proposed mutation operator contains a dynamic base vector that consists of two individuals, one is the current optimal individual while the other, called elite individual, is the best one among three randomly selected individuals, and they are dynamically combined by a weight parameter associated with the evolution process and the ranking status of the elite individual in the current population. Moreover, the scale factor and crossover rate in DCDE depend on the combined effect of a population-level parameter and one individual-level parameter, respectively. Both mutation operator and control parameters in DCDE are designed to achieve an appropriate balance between global exploration ability and local exploitation ability. To evaluate the performance of DCDE, comparison experiments are conducted with five state-of-the-art DE variants and three non-DE algorithms on solving 29 functions in IEEE CEC 2017 benchmark suite. The comparison results indicate that the proposed DCDE is significantly better than, or at least comparable to the adopted competitors.
KW - Differential evolution
KW - Mutation operator
KW - Numerical optimization
KW - Two-level parameter
UR - http://www.scopus.com/inward/record.url?scp=85121648834&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2021.116298
DO - 10.1016/j.eswa.2021.116298
M3 - Article
AN - SCOPUS:85121648834
SN - 0957-4174
VL - 192
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 116298
ER -