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Stochastic fractal search algorithm in permutation flowshop scheduling problem

机译:置换流程调度问题中的随机分数算法

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In this paper, permutation flowshop scheduling problem is solved using stochastic fractal search algorithm to find a sequence of jobs minimizing makespan. SFS algorithm is inspired by the phenomenon of successful growth which uses a mathematical concept called fractal. The performance of SFS algorithm to solve permutation flowshop scheduling problem was tested using standard benchmark problems of Taillard and compared with other optimization algorithms. The results have shown that the proposed SFS algorithm performs better than other algorithms on given benchmark problems for finding the best solution found so far in minimizing makespan. Moreover, comparing with the best-known result, SFS successfully provides solutions which are near-optimal solutions.
机译:在本文中,使用随机分形搜索算法解决了置换流程调度问题,找到了一系列最小化Mapespan的作业。 SFS算法受到使用数学概念称为分形的成功增长现象的启发。 使用Taillard的标准基准问题测试了SFS算法解决置换流量调度问题的性能,并与其他优化算法进行比较。 结果表明,所提出的SFS算法比在给定基准问题上的其他算法上表现更好,以便在最小化Makespan中找到最佳解决方案。 此外,与最着名的结果相比,SFS成功提供了近最佳解决方案的解决方案。

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