Improved differential evolution algorithm with adaptive mutation and control parameters

2011 ◽  
Author(s):  
Li Hui-rong ◽  
Gao Yue-lin ◽  
Li Chao ◽  
Zhao Peng-jun
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Adaptive Mutation ◽  
Control Parameters ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
And Control

A differential evolution algorithm with self-adapting strategy and control parameters

2011 ◽  
Vol 38 (1) ◽  
pp. 394-408 ◽  
Author(s):  
Quan-Ke Pan ◽  
P.N. Suganthan ◽  
Ling Wang ◽  
Liang Gao ◽  
R. Mallipeddi
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Control Parameters ◽  
Evolution Algorithm ◽  
And Control

scholarly journals Application of Improved Differential Evolution Algorithm in Solving Equations

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guiying Ning ◽  
Yongquan Zhou
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Convergence Speed ◽  
Evolutionary Strategy ◽  
Adaptive Mutation ◽  
Local Optimum ◽  
Algebraic Equations ◽  
Evolution Algorithm ◽  
Roots Of Equations ◽  
Improved Differential Evolution Algorithm

AbstractThe problem of finding roots of equations has always been an important research problem in the fields of scientific and engineering calculations. For the standard differential evolution algorithm cannot balance the convergence speed and the accuracy of the solution, an improved differential evolution algorithm is proposed. First, the one-half rule is introduced in the mutation process, that is, half of the individuals perform differential evolutionary mutation, and the other half perform evolutionary strategy reorganization, which increases the diversity of the population and avoids premature convergence of the algorithm; Second, set up an adaptive mutation operator and a crossover operator to prevent the algorithm from falling into the local optimum and improve the accuracy of the solution. Finally, classical high-order algebraic equations and nonlinear equations are selected for testing, and compared with other algorithms. The results show that the improved algorithm has higher solution accuracy and robustness, and has a faster convergence speed. It has outstanding effects in finding roots of equations, and provides an effective method for engineering and scientific calculations.

scholarly journals An Improved Differential Evolution Algorithm Based on Adaptive Parameter

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Differential Evolution ◽  
Optimization Technique ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Control Parameters ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Evolution Stage ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The differential evolution (DE) algorithm is a heuristic global optimization technique based on population which is easy to understand, simple to implement, reliable, and fast. The evolutionary parameters directly influence the performance of differential evolution algorithm. The adjustment of control parameters is a global behavior and has no general research theory to control the parameters in the evolution process at present. In this paper, we propose an adaptive parameter adjustment method which can dynamically adjust control parameters according to the evolution stage. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

Sign in / Sign up

Export Citation Format

Share Document