An Improved Bat Algorithm Based on Starling Flock Behavior

Bat algorithm (BA) is a new metaheuristic algorithm.However,the standard BA has some shortcomings,e.g.,low convergence precision and easily relapsing into the local optima.In this work,by introducing the collective behavior of the starling group into BA algorithm,the searching range of the standard BA algorithm can be effectively improved.Besides,a linear decreasing weight is introduced to balance the global search and the local search.Simulation results from Benchmark functions show that the improved algorithm can effectively avoid the local optimum and attain higher convergence precision.