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Soft Computing
Published in: Soft Computing 16/2018

04-12-2017 | Focus

Modified bat algorithm based on covariance adaptive evolution for global optimization problems

Authors: Xian Shan, Huijin Cheng

Published in: Soft Computing | Issue 16/2018

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Abstract

Bat algorithm is a newly proposed swarm intelligence algorithm inspired by the echolocation behavior of bats, which has been successfully used in many optimization problems. However, due to its poor exploration ability, it still suffers from problems such as premature convergence and local optimum. In order to enhance the search ability of the algorithm, we propose an improved bat algorithm, which is based on the covariance adaptive evolution process. The information included in the covariance adaptive evolution diversifies the search directions and sampling distributions of the population, which is of great benefit to the search process. The proposed approaches have been tested on a set of benchmark functions. Experimental results indicate that the proposed algorithm obtains superior performance over the majority of the test problems.
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Appendix
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Metadata
Title
Modified bat algorithm based on covariance adaptive evolution for global optimization problems
Authors
Xian Shan
Huijin Cheng
Publication date
04-12-2017
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 16/2018
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2952-5

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