An improved adaptive differential evolution algorithm for continuous optimization

2016 ◽  
Vol 44 ◽  
pp. 1-12 ◽  
Author(s):  
Wenchao Yi ◽  
Yinzhi Zhou ◽  
Liang Gao ◽  
Xinyu Li ◽  
Jianhui Mou
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

scholarly journals An Adaptive Differential Evolution Algorithm with Restart for Solving Continuous Optimization Problems

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
General Purpose ◽  
Adaptive Parameter ◽  
Adaptive Differential Evolution ◽  
Use Efficiency ◽  
Evolution Algorithm ◽  
Continuous Optimization Problems

A new adaptive differential evolution algorithm with restart (ADE-R) is proposed as a general-purpose method for solving continuous optimization problems. Its design aims at simplicity of use, efficiency and robustness. ADE-R simulates a population evolution of real vectors using vector mixing operations with an adaptive parameter control based on the switching of two selected intervals of values for each scaling factor and crossover rate of the basic differential evolution algorithm. It also incorporates a restart technique to supply new contents to the population to prevent premature convergence and stagnation. The method is tested on several benchmark functions covering various types of functions and compared with some well-known and state-of-art methods. The experimental results show that ADE-R is effective and outperforms the compared methods.

Three modified versions of differential evolution algorithm for continuous optimization

2010 ◽  
Vol 15 (4) ◽  
pp. 803-830 ◽  
Author(s):  
Morteza Alinia Ahandani ◽  
Naser Pourqorban Shirjoposh ◽  
Reza Banimahd
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
Evolution Algorithm
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