In response to the challenges faced by traditional genetic algorithms when addressing the Flexible Job Shop Sched-uling Problem(FJSP),such as poor dynamic adaptability of populations and susceptibility to local optima,proposing an improved genetic algorithm that integrates a random-restart hill climbing operator.Enhancing the ability to exchange information between populations through dual population crossing.Introducing convergence criteria to maintain population diversity while preventing ex-cessive disruption of superior individuals within the population.By incorporating the principles of random-restart hill climbing,the algorithm's local search capabilities are significantly improved.Simulation experiments demonstrate that the proposed algorithm consistently exhibits strong optimization performance across problems of varying scales.
关键词
柔性车间调度/改进遗传算法/接受准则/随机重启爬山算子
Key words
flexible job shop scheduling/improved genetic algorithm/acceptance criteria/random-restart hill climbing operator
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