Abstract: Quantum-behaved particle swarm optimization has better performance compared with particle swarm optimization, but it still has the problem of getting trapped into local optimum with premature convergence. According to the above problem, a hybrid algorithm included quantum-behaved particle swarm optimization and bat algorithm was proposed. On the one hand, the random walk strategy of bat algorithm was used to avoid getting into local optimum, on the other hand, the speed changing of bats’ sound was learned to transform the factor of quantum-behaved particle swarm optimization. The proposed algorithm was tested on five benchmark functions and a mould job shop scheduling example, compared with PSO (Particle Swarm Optimization) and QPSO (Quantum-Behaved Particle Swarm Optimization). The simulated experimental results indicate the validity and superiority of the hybrid algorithm.

Key words: bat algorithm, quantum-behaved particle swarm optimization, scheduling optimization, mould job shop