This paper presents a hybrid PSO (HPSO) algorithm to the solution of job-shop fuzzy scheduling problem. The proposed algorithm uses processing encoding random key to generate initial population, takes parameter uniformity crossover operator as particle swarmȁ9;s update operator, and evaluates each particle properties according to customer satisfaction, and then completes particle individual extremum and neighborhood extremum update according to the above-mentioned evaluation. The algorithm utilizes neighborhood knowledge to direct its local search procedure, which overcome the blindness or randomness introduced by meta-heuristics. Simulation results show that HPSO algorithm can speed up convergence as well as improve the quality of shop scheduling solution.
展开▼
