An Adaptive Parameter Control for the Differential Evolution Algorithm

2009 ◽  
pp. 375-382
Author(s):  
Gilberto Reynoso-Meza ◽  
Javier Sanchis ◽  
Xavier Blasco
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Parameter Control ◽  
Adaptive Parameter ◽  
Evolution Algorithm ◽  
Adaptive Parameter Control

scholarly journals An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Tae Jong Choi ◽  
Chang Wook Ahn ◽  
Jinung An
Keyword(s):  
Differential Evolution ◽  
Control Method ◽  
Differential Evolution Algorithm ◽  
Cauchy Distribution ◽  
Parameter Control ◽  
Control Parameters ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Parameter Values ◽  
Adaptive Parameter Control

Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals’ parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

scholarly journals Multiscale Cooperative Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yongzhao Du ◽  
Yuling Fan ◽  
Xiaofang Liu ◽  
Yanmin Luo ◽  
Jianeng Tang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Early Stage ◽  
Differential Evolution Algorithm ◽  
Search Range ◽  
Adaptive Parameter ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
The Individual

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.

APDDE: self-adaptive parameter dynamics differential evolution algorithm

2016 ◽  
Vol 22 (4) ◽  
pp. 1313-1333 ◽  
Author(s):  
Hong-bo Wang ◽  
Xue-na Ren ◽  
Guo-qing Li ◽  
Xu-yan Tu
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Adaptive Parameter ◽  
Evolution Algorithm ◽  
Self Adaptive

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.

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.

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