Adaptive Differential Evolution based on Exploration and Exploitation Control

Hao BAI1; Changwu Huang2; Xin Yao2

10.1109/CEC45853.2021.9504876

2021-08-09

2021 IEEE Congress on Evolutionary Computation (CEC)

978-1-7281-8394-7

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021)

41-48

28 June-1 July 2021

Kraków, Poland

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Search operator design and parameter tuning are essential parts of algorithm design. However, they often involve trial-and-error and are very time-consuming. A new differential evolution (DE) algorithm with adaptive exploration and exploitation control (AEEC-DE) is proposed in this work to tackle this challenge. The proposed method improves the performance of DE by automatically selecting trial vector generation strategies (both mutation and crossover operators) and dynamically generating the associated control parameter values. A probability-based exploration and exploitation measurement is introduced to estimate whether the state of each newly generated individual is in exploration or exploitation. The state of historical individuals is used to assess the exploration and exploitation capabilities of different generation strategies and parameter values. Then, the strategies and parameters of DE are adapted following the common belief that evolutionary algorithms (EAs) should start with exploration and then gradually change into exploitation. The performance of AEEC-DE is evaluated through experimental studies on a set of test problems and compared with several state-of-the-art adaptive DE variants.

Algorithm Configuration Differential Evolution Parameter Control Exploration and Exploitation

通讯

英语

EI ; CPCI-S

Guangdong Basic and Applied Basic Research Foundation[2019A1515110575]

Computer Science ; Engineering ; Mathematical & Computational Biology ; Operations Research & Management Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Mathematical & Computational Biology ; Operations Research & Management Science

WOS:000703866100006

20220711650793

Genetic algorithms ; Natural resources exploration

人工提交

1.Laboratory of Mechanics of Normandy (LMN), INSA Rouen Normandy, 76000 Rouen, France
2.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Hao BAI,Changwu Huang,Xin Yao. Adaptive Differential Evolution based on Exploration and Exploitation Control[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:41-48.