A hybrid optimizer based on backtracking search and differential evolution for continuous optimization

2021 ◽  
pp. 1-31
Author(s):  
Yiğit Çağatay Kuyu ◽  
Enrique Onieva ◽  
Pedro Lopez-Garcia
Keyword(s):  
Differential Evolution ◽  
Continuous Optimization ◽  
Backtracking Search

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

scholarly journals A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Lijin Wang ◽  
Yiwen Zhong ◽  
Yilong Yin ◽  
Wenting Zhao ◽  
Binqing Wang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Iteration Process ◽  
Global Optimum ◽  
Backtracking Search ◽  
De Algorithm ◽  
Search Optimization ◽  
Backtracking Search Optimization Algorithm ◽  
Mutation Strategies ◽  
Multimodal Problems

The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

scholarly journals L-SHADE with Alternative Population Size Reduction for Unconstrained Continuous Optimization

2020 ◽  
Author(s):  
Christopher Renkavieski ◽  
Rafael Stubs Parpinelli
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Numerical Optimization ◽  
Continuous Optimization ◽  
Size Reduction

Differential Evolution (DE) is a powerful and versatile algorithmfor numerical optimization, but one of its downsides is its numberof parameters that need to be tuned. Multiple techniques have beenproposed to self-adapt DE’s parameters, with L-SHADE being oneof the most well established in the literature. This work presentsthe A-SHADE algorithm, which modifies the population size reductionschema of L-SHADE, and also EB-A-SHADE, which applies amutation strategy hybridization framework to A-SHADE. Thesealgorithms are applied to the CEC2013 benchmark set with 100dimensions, and it’s shown that A-SHADE and EB-A-SHADE canachieve competitive results.

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