A Differential Evolution Algorithm with a Variable Neighborhood Search for Constrained Function Optimization

2015 ◽  
pp. 171-184 ◽  
Author(s):  
M. Fatih Tasgetiren ◽  
P. N. Suganthan ◽  
Sel Ozcan ◽  
Damla Kizilay
Keyword(s):  
Differential Evolution ◽  
Variable Neighborhood Search ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Neighborhood Search ◽  
Evolution Algorithm ◽  
Constrained Function

A Hybrid Differential Evolution Algorithm Based on Dynamic Variable Neighborhood Search for Permutation Flowshop Scheduling Problem

2016 ◽  
Vol 835 ◽  
pp. 847-857 ◽  
Author(s):  
Wen Bo Liu
Keyword(s):  
Differential Evolution ◽  
Variable Neighborhood Search ◽  
Differential Evolution Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Dynamic Variable ◽  
Permutation Flowshop ◽  
Evolution Algorithm ◽  
Flowshop Scheduling Problem

Permutation flowshop scheduling problem (PFSP) is a classical NP-hard combinatorial optimization problem, which provides a challenge for evolutionary algorithms.Since it has been shown that simple evolutionary algorithms cannot solve the PFSP efficiently, local search methods are often adopted to improve the exploitation ability of evolutionary algorithms. In this paper, a hybrid differential evolution algorithm is developed to solve this problem. This hybrid algorithm is designed by incorporating a dynamic variable neighborhood search (DVNS) into the differential evolution. In the DVNS, the neighborhood is based on multiple moves and its size can be dynamically changed from small to large so as to obtain a balance between exploitation and exploration. In addition, a population monitoring and adjusting mechanism is also incorporated to enhance the search diversity and avoid being trapped in local optimum.Experimental results on benchmark problems illustrated the efficiency of the proposed algorithm.

An Efficient Differential Evolution Algorithm For Function Optimization

2013 ◽  
Vol 12 (3) ◽  
pp. 444-448 ◽  
Author(s):  
Chao-Xue Wang ◽  
Chang-Hua Li ◽  
Hui Dong ◽  
Fan Zhang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Evolution Algorithm

Fine-Grained Differential Evolution Algorithm for Multi-Dimensional Function Optimization

2012 ◽  
Vol 11 (1) ◽  
pp. 315-320
Author(s):  
Yiwen Zhong ◽  
Xiaoyu Lin ◽  
Lijin Wang ◽  
Hui Zhang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Fine Grained ◽  
Evolution Algorithm

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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