Enhancing local search of differential evolution algorithm for high dimensional optimization problem

2015 ◽  
Author(s):  
Xiao-gang Dong ◽  
Chang-shou Deng ◽  
Yan Zhang ◽  
Yu-cheng Tan
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
High Dimensional ◽  
Evolution Algorithm ◽  
Dimensional Optimization

scholarly journals Application of Differential Evolution in the Solution of Stiff System of Ordinary Differential Equations

2020 ◽  
Vol 8 (1) ◽  
pp. 01-08
Author(s):  
Ashiribo Senapon Wusu ◽  
Olusola Olabanjo ◽  
Benjamin Aribisala
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Approximate Solutions ◽  
Constrained Optimization Problem ◽  
Stiff System ◽  
Order Ordinary Differential Equation ◽  
First Order ◽  
Evolutionary Optimization Algorithms ◽  
Evolution Algorithm

In recent times, the adaptation of evolutionary optimization algorithms for obtaining optimal solutions of many classical problems is gaining popularity. In this paper, optimal approximate solutions of initial--valued stiff system of first--order Ordinary Differential Equation (ODE) are obtained by converting the ODE into constrained optimization problem. The later is then solve via differential evolution algorithm. To illustrate the efficiency of the proposed approach, two numerical examples were considered. This approach showed significant improvement on the accuracy of the results produced compared with existing methods discussed in literature.

Niche Differential Evolution Algorithm and its Application in Multimodal Function Optimization

2011 ◽  
Vol 308-310 ◽  
pp. 2431-2435 ◽  
Author(s):  
Na Li ◽  
Yuan Xiang Li ◽  
Zhi Guo Huang ◽  
Yong Wang
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Function Optimization ◽  
Multimodal Optimization ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
Optimal Value ◽  
Evolution Algorithm

In multimodal optimization, the original differential evolution algorithm is easy to duplicate and miss points of the optimal value. To solve this problem, a modified differential evolution algorithm, called niche differential evolution (NDE), is proposed. In the algorithm, the basic differential evolution algorithm is improved based on the niche technology. The rationality to construct the proposed algorithm is discussed. Shubert function, a representative multimodal optimization problem is used to verify the algorithm. The results show that the proposed algorithm can find all global optimum points quickly without strict request for parameters, so it is a good approach to find all global optimum points for multimodal functions.

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