An Improved Bat Algorithm for Job Shop Scheduling Problem

Job shop scheduling problem is one of the hardest combinatorial optimization problems. Many approaches such as Genetic Algorithm, Particle Swarm Optimization Algorithm and heuristic algorithm have been used to solve this problem. Unfortunately, those algorithms are easy to get a local optimal solution. In this paper, an Improved Bat Algorithm was proposed to solve job shop scheduling problem, it can effectively avoid premature convergence. Moreover, it can speed up the convergence and improves the ability to find global optimal solution. Compared with Bat Algorithm and Particles Swarm Algorithm, the simulation results show that the Improved Bat Algorithm is efficacious to minimize makespan and accurate to find the optimal solution.