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An ensemble bat algorithm for large-scale optimization
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DOI：10.1007/s13042-019-01002-8.png)
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Abstract
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It is difficult for the bat algorithm (BA) to retain good performance with increasing problem complexity and problem. In this paper, an ensemble BA is proposed to solve large-scale optimization problems (LSOPs) by introducing the integration ideas. The characteristics of six improved BA strategies are taken into account for the ensemble strategies. To fuse these strategies perfectly, the probability selection mechanisms, including the constant probability and dynamic probability, are designed by adjusting the odds of different strategies. To verify the performance of the algorithm in this paper, the proposed algorithm is applied to solve numerical optimization problems on benchmark functions with different dimensions. Then, the best ensemble BA is selected by comparing the constant probabilities and dynamic probabilities. The selected algorithm is compared with other excellent swarm intelligence optimization algorithms. Additionally, the superiority of the proposed algorithm is confirmed for solving LSOPs.
Keywords:
Bat algorithm
Large-scale optimization
Ensemble strategy
Benchmark function
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