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首页> 外文期刊>International Journal of Innovative Computing Information and Control >SOLVING THE SHORTEST PATH PROBLEM WITH IMPRECISE ARC LENGTHS USING A TWO-STAGE TWO-POPULATION GENETIC ALGORITHM
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SOLVING THE SHORTEST PATH PROBLEM WITH IMPRECISE ARC LENGTHS USING A TWO-STAGE TWO-POPULATION GENETIC ALGORITHM

机译：两阶段两种群遗传算法用不精确的弧长解决最短路径问题

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摘要

This study investigates how to solve the shortest path problem with imprecise arc lengths using a two-stage two-population genetic algorithm (GA). This approach can conveniently represent imprecise numerical quantities, and therefore, it is able to handle imprecise arc lengths. In its first stage, the proposed GA simulates a fuzzy number by partitioning an imprecise arc length into a finite number of subintervals. Each subinterval represents a partition point. A random real number in [0, 1] is first assigned to each partition point. The GA then evolves the values in each partition point, with the final values in each partition point representing the membership grades of that fuzzy number. Thus, it is possible to obtain estimated values for the originally imprecise arc lengths, and the fuzzy problem becomes a defuzzified instance. The second stage of the GA is to search for the best solution to the defuzzified instance using a scheme in which two candidate populations evolve simultaneously. The first population comprises a set of feasible candidate solutions, and the second population consists of infeasible candidate solutions. The two solution populations are separately maintained and evolved, but their offspring may flow from one population into the other. Experimental results show that the proposed two-stage two-population GA approach obtains better results than other fuzzy shortest path approaches.
机译：本研究研究如何使用两阶段两种群遗传算法（GA）解决弧长度不精确的最短路径问题。这种方法可以方便地表示不精确的数值，因此，它可以处理不精确的电弧长度。在其第一阶段，拟议的遗传算法通过将不精确的弧长划分为有限数量的子间隔来模拟模糊数。每个子间隔代表一个分区点。首先将[0，1]中的随机实数分配给每个分区点。然后，GA会演化每个分区点中的值，每个分区点中的最终值代表该模糊数的隶属度。因此，有可能获得对于最初不精确的电弧长度的估计值，并且模糊问题成为去模糊的实例。遗传算法的第二阶段是使用两个候选种群同时进化的方案来寻找去模糊化实例的最佳解决方案。第一个总体包括一组可行的候选解决方案，第二个总体由不可行的候选解决方案组成。这两个解决方案种群分别得到维护和进化，但它们的后代可能会从一个种群流向另一个种群。实验结果表明，所提出的两阶段两种群遗传算法比其他模糊最短路径方法获得更好的结果。

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