Open AccessA New Adaptive Differential Evolution Algorithms
Author(s) -
Yu Tian,
Tinghui Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1437/1/012022
Subject(s) - convergence (economics) , crossover , algorithm , differential evolution , operator (biology) , mutation , computer science , population , mathematics , mathematical optimization , artificial intelligence , biochemistry , chemistry , demography , repressor , sociology , transcription factor , economics , gene , economic growth
In this paper, we describe a New Adaptive Differential Evolution algorithm (NADE) based on adaptive mutation operator, crossover operator and new mutation strategy. It is mainly aimed at the existence of individual aggregation and the reduction of population diversity in the calculation process of Differential Evolution algorithm (DE), which makes the algorithm easy to get early. The problems of ripeness, slow convergence speed and low convergence accuracy are improved. The improved differential evolution algorithm is tested by five commonly used test functions, and the test results are compared with the other three algorithms. The results show that the proposed algorithm performs better in convergence speed, convergence precision and global convergence ability.