摘要
提出一种用协同进化遗传算法求解作业车间调度问题的新方法.车间调度问题用传统的启发式算法很难求得最优解.协同进化遗传算法模拟生物界物种之间的竞争、捕食、共生及其相互作用下,各物种协同进化,使整个生态系统由低级向高级进化的过程.协同进化算法与传统的遗传算法相比,不仅加快了算法的收敛速度,且可提高算法的搜索能力,避免算法陷入局部最优.特殊的交叉操作更使所求得的解都为合法解.实例证明协同进化遗传算法是行之有效的算法.
A novel genetic algorithm for job-shop problems with coevolutionary model has been presented.Job-shop scheduling is a NP-hard problem. It is demonstrated that coevolutionary genetic algorithm(COGA) simulating the competition of ecosystem is capable of speeding up the computation and finding the best solution easier.An effective crossover operation for operation-based representation is used to guarantee the feasibility of the solution.Simulation results demonstrate the effectiveness of this proposed algorithm. The optimization performance is improved significantly in comparison with the standard genetic algorithm.
出处
《深圳大学学报（理工版）》
EI
CAS
2004年第3期272-275,共4页
Journal of Shenzhen University(Science and Engineering)
