A Bat Algorithm Based on Centroid Strategy

摘要

The standard bat algorithm is slow convergence and low precision. To overcome this shortcoming, we propose a novel variant of bat optimization algorithm based on centroid strategy. The proposed algorithm has a better global searching capability because the centroid strategy can effectively prevent it falling into a local optimum. Two typical test functions are employed to test its performance. Simulation results show our proposal is both effective and efficient than three other comparison algorithms. Moreover, for high-dimensional function optimization, our proposed algorithm also has excellent approximation performance.