Improved genetic algorithm with integrated random-restart hill climbing operator for solving FJSP
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.
flexible job shop schedulingimproved genetic algorithmacceptance criteriarandom-restart hill climbing operator
万方数据
维普
