Bat algorithm based on simulated annealing and Gaussian perturbations
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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.
KeywordsAlgorithm Bat algorithm Swarm intelligence Optimization Simulated annealing
The authors would like to thank the financial support by Shaanxi Provincial Soft Science Foundation (2012KRM58) and Shaanxi Provincial Education Grant (12JK0744 and 11JK0188).
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