An improved bat algorithm based on multi-subpopulation search strategy

Bat algorithm (BA) is a novel swarm intelligence optimization algorithm inspired by the behavior of bat hunting for prey and has been applied in many optimization problems. However, BA has some shortcomings including easy to fall into local optima and low precision of solution when solving some complex problem. In order to enhance its performance, a multi-subpopulation bat optimization algorithm (MSPBA) is proposed in this paper. The specific idea of bat algorithm improvement is to divide the population into three subgroups, each using different search strategies. The first subgroup mainly performs global search to improve the global exploration ability of the algorithm. The second subgroup mainly performs local search to improve the accuracy of the algorithm. The third subgroup is mainly to enhance population diversity and avoid falling into local optimum. 10 standard benchmark functions are used to illustrate the performance of the proposed algorithm by comparing with DBA, BA, PSO, DE and CS. The simulation results show the superiority of MSPBA.