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Neural Computing and Applications
Published in: Neural Computing and Applications 2/2014

01-08-2014 | Original Article

Bat algorithm based on simulated annealing and Gaussian perturbations

Authors: Xing-shi He, Wen-Jing Ding, Xin-She Yang

Published in: Neural Computing and Applications | Issue 2/2014

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Abstract

Bat algorithm (BA) is a new stochastic optimization technique for global optimization. In the paper, we introduce both simulated annealing and Gaussian perturbations into the standard bat algorithm so as to enhance its search performance. As a result, we propose a simulated annealing Gaussian bat algorithm (SAGBA) for global optimization. Our proposed algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality, but also speeds up the global convergence rate. We have used BA, simulated annealing particle swarm optimization and SAGBA to carry out numerical experiments for 20 test benchmarks. Our simulation results show that the proposed SAGBA can indeed improve the global convergence. In addition, SAGBA is superior to the other two algorithms in terms of convergence and accuracy.
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Metadata
Title
Bat algorithm based on simulated annealing and Gaussian perturbations
Authors
Xing-shi He
Wen-Jing Ding
Xin-She Yang
Publication date
01-08-2014
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 2/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1518-4

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