Bat algorithm (BA) is a new approach designed by imitating bat’s behavior of searching and capturing preys. The existing results have demonstrated the effectiveness and efficiency in comparison with other heuristic algorithms such as genetic algorithms and particle swarm optimization. In this paper, we design a novel framework for bat algorithm named two-stage bat algorithm (TSBA) using a trade-off strategy which balances the relationship between exploration and exploitation at the most extent. Inspired by the multi-population methods (e.g., artificial bee colony), we not only concern some technologies to avoid premature inevitably encountered when using BA, but also use a trade-off strategy to improve the comprehensive search performance for optimization. Some typical test sets which consist of 27 benchmark functions are utilized in comparative experiment, and the simulation results in terms of convergence rate and accuracy illustrate that the TSBA has a competitive performance than other swarm intelligent optimization algorithms. In addition, the proposed algorithm will not lend to the tremendous increase in computing time and thus will be a powerful tool in practical applications.
['Boyang Zhang', 'Haiwen Yuan', 'Lingjie Sun', 'Jian Shi', 'Zhao Ma', 'Limei Zhou']
A two-stage framework for bat algorithm