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A Cooperative Co-evolutionary Quantum Particle Swarm Optimizer based on Simulated Annealing for Job Shop Scheduling Problem

Bin Jiao, Shaobin Yan


Job shop scheduling problem, a proved typical NP-hard problem, has been paid attention to and researchers have proposed various optimization algorithms. In this paper, a novel intelligent algorithm (SACQPSO) mixed with simulated annealing, cooperative co-evolution thought, quantum-behaved theory and particle swarm optimization algorithm is put forward, which could not only enhance the capacity of searching the best solution and increase the diversity of particles owing to co-operative co-evolution thought and quantum-behaved theory, but also strengthen the ability of global searching as the result of simulated annealing. Eventually, several typical JSSPs are solved effectively through SACQPSO, which is especially for problems on a large scale. The experimental results show that SACQPSO is more effective and feasible in comparison with PSO, QPSO, CPSO and CQPSO.


Simulated Annealing; Cooperative Co-evolutionary approach; Quantum-behaved Theory; Particle swarm optimization; Job Shop Scheduling Problem

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