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On the population diversity control of evolutionary algorithms for production scheduling problems

机译:论生产调度问题进化算法的人口多样性控制

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In the literature, many evolutionary algorithms have been proposed for the production scheduling problems such as genetic algorithm, particle swarm optimization, differential evolution, and so on. However, these algorithms mainly focus on the efficiency of local search methods but seldom tackle the control of population diversity. Therefore, this paper aims to check whether the population diversity control strategy can significantly impact the performance of evolutionary algorithms for the production scheduling problems. With this aim in mind, a population diversity control strategy was proposed and applied in a basic evolutionary algorithm. The computational results on two representative production scheduling problems, i.e., the single machine scheduling and the permutation flowshop scheduling, show that the proposed population diversity control strategy can significantly improve the basic evolutionary algorithm and that this algorithm with the strategy (but without local search) can even be competitive with other powerful evolutionary algorithms with local search in the literature.
机译:在文献中,已经提出了许多进化算法,用于生产调度问题,例如遗传算法,粒子群优化,差分演化等。然而,这些算法主要关注本地搜索方法的效率,但很少地解决人口多样性的控制。因此,本文旨在检查人口多样性控制策略是否会影响进化算法的生产调度问题的性能。借助这一目标,提出了一种分集控制策略并以基本的进化算法应用。两个代表性生产调度问题的计算结果,即单机调度和排列流程调度,表明所提出的群体分集控制策略可以显着提高基本进化算法,并将该算法与策略(但没有本地搜索)甚至可以与其他强大的进化算法有竞争力,在文献中具有本地搜索。

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