Novel Binary Encoding Differential Evolution Algorithm

Deng, Changshou; Zhao, Bingyan; Yang, Yue; Peng, Hu; Wei, Qiming

doi:10.1007/978-3-642-21515-5_49

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…Using PSO in [92], they solved the -queens problems, and, in [90], Liu et al solved large-scale power systems. In [93], a differential evolution algorithm was applied to knapsack problems, and,…”

Section: (20)mentioning

confidence: 99%

Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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Section: (20)mentioning

confidence: 99%

Putting Continuous Metaheuristics to Work in Binary Search Spaces

et al. 2017

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“…The angle modulated DE (AMDE) makes use of angle modulation, a technique derived from the telecommunications industry, to implement a mapping between binary and continuous spaces (Engelbrecht & Pampara, 2007). Many approaches operate the binary space directly and design new mutation schemes for the representation (Chen et al, 2015;Deng et al, 2011;Peng et al, 2016). Some alternative approaches don't design analogs of operators in continuous DE, but develop new operators while maintaining the general DE's scheme (Banitalebi et al, 2016;Wang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A Novel Binary DE Based on the Binary Search Space Topology

Sopov¹

2023

European Proceedings of Computers and Technology

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Differential evolution is known as a simple and well-argued evolutionary algorithm that demonstrates the high performance in many hard black-box optimization problems with continuous variables. The main feature of differential evolution is the difference-based mutation. The mutation explores the search space using the distribution of points in the population and usually can well adapt to the objective function landscape. There exist some modifications of differential evolution for the binary search space. The proposed approach involves the understanding of the binary space topology for developing a better analogue of the difference-based mutation. We have compared the proposed binary differential evolution algorithm with the standard binary genetic algorithm using a set of binary test problems, including hard deceptive problems. The preliminary experimental results shows that new binary differential evolution is a competitive search algorithm and outperforms the binary genetic algorithm in reliability for some problems but yields it in the required number of function evaluations. The proposed approach for developing mutation in DE can be expanded to all other mutation schemes for increasing the performance.

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“…For instance, Ozturk et al [36] proposed an efficient binary variant of the ABC algorithm where the genetic operators are employed to design special search strategies. • Binary operators : As the search space consists of binary arrays, it is also possible to make use of binary operators such as xor , and , or , and not operators to generate trial solutions [11] . • Measure of dissimilarity : The techniques in this approach define a method to calculate the dissimilarity of two binary arrays.…”

Section: Introductionmentioning

confidence: 99%