Modified bat algorithm based on covariance adaptive evolution for global optimization problems
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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.
KeywordsBat algorithm Swarm intelligence Covariance adaptive evolution
This work was supported in part by the National Nature Science Foundation of China under Grants 61402534, by the Shandong Provincial Natural Science Foundation, China under grant ZR2014FQ002, and by the Fundamental Research Funds for the Central Universities under grants 16CX02010A.
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
This article does not contain any studies with human participants or animals performed by any of the authors.
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