A novel hybrid bat algorithm for solving continuous optimization problems

The Bat Algorithm (BA), which is a global optimization method, performs poorly on complex continuous optimization problems due to BA's disadvantages such as the premature convergence problem. In this paper, we propose a novel Hybrid Bat Algorithm (HBA) to improve the performance of BA. Three modification methods are incorporated into the standard BA to enhance the local search capability and the ability to escape from local optimum traps. The effectiveness and contribution of these three modification methods are analyzed by using classical benchmark functions. Moreover, the performance of HBA is evaluated on the numerical functions from the CEC 2014 test suite and compared with those of well-known optimization algorithms. The statistical test results indicate that HBA is a significant improvement.