An improved Quantum Particle Swarm Optimization and its application

摘要

Compared to other intelligent optimization algorithms, Quantum Particle Swarm Optimization (QPSO) possesses the characteristics like rapid convergence rate and outstanding global optimization performance etc. It is more applicable to solve workshop scheduling problems. The article proposes the strategy of improved dynamic regulation of rotation angle to solve multi-objective FJSP problems on the basis of Quantum Particle Swarm Optimization. The method can ensure the position with large variation of adaptive value not over optimal regulation measure, increase the capability to search optimal solution at the position with small variation of adaptive value, and verify the effectiveness of new algorithm through simulation experiment.