A Novel Binary DE Based on the Binary Search Space Topology
Abstract
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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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Sopov, E. (2023). A Novel Binary DE Based on the Binary Search Space Topology. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 328-335). European Publisher. https://doi.org/10.15405/epct.23021.40