Chaotic-encode Quantum PSO Algorithm for Flexible Job-shop Scheduling Problem
To solve the flexible job-shop scheduling problem(FJSP),a chaotic-encode quantum PSO(CQPSO)algorithm is proposed.Aiming at the premature convergence of particles to local optimum in standard QPSO,the methods for computing the adaptive contraction-expansion coefficient and mean best position using fitness values of associated particles are proposed to improve the global search ability of QPSO.Through chaotic boundary variation strategy,the probability of a large number of particles gathering at the boundary is reduced and the population diversity is increased to enhance the ability of searching the optimal solution.According to the iterative property of QPSO,a chaotic-encode strategy is designed.The proposed CQPSO is applied to solve FJSP and the result is compared with QPSO,PSO,and hybrid genetic algorithm result on several benchmarks to confirm the performance.The experimental results show that CQPSO has the better stability and the stronger optimization ability.
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