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Operators of the Two-Part Encoding Genetic Algorithm in Solving the Multiple Traveling Salesmen Problem

机译:求解多重旅行商问题的两部分编码遗传算法的算子

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The multiple traveling salesmen problem (mTSP) considers the $m$ salesmen to visit $n$ cities. This problem involves the assignment of the salesmen to some locations and we have to optimize the sequence within the route, so it is even harder than the traveling salesman problem (TSP) in nature. As a result, there are some algorithms used to solve the mTSP when the problem size is large. Particularly, genetic algorithm (GA) is quite famous in solving this problem while the problem size is large. When we compare the major existing encoding methods for mTSP, the best approach could be the two-part chromosome encoding due to its solution space is the smallest. The two parts are responsible for the sequence and the number of cities should be visited by each salesman. However, because the two-part chromosome technique is the recently proposed encoding method, the better combination of the crossover operators and mutation operators have not studied for this encoding method. As a result, this paper investigates the genetic operators could be used for this purpose by design-of-experiments (DOE). The appropriate genetic operators are suggested in this paper and it could be used to applied in the GA which employs the two-part chromosome encoding technique.
机译:多重旅行推销员问题(mTSP)考虑$ m $推销员访问$ n $城市。这个问题涉及到将销售员分配到某些位置,并且我们必须优化路线中的顺序,因此,这实际上比旅行业务员问题(TSP)还要困难。结果,当问题规模很大时,有一些算法可用于解决mTSP。特别是,遗传算法(GA)在解决此问题时非常著名,而问题却很大。当我们比较现有的主要mTSP编码方法时,最好的方法可能是两部分染色体编码,因为它的解决方案空间最小。这两个部分负责顺序,每个销售员应拜访的城市数量。但是,由于两部分染色体技术是最近提出的编码方法,因此尚未研究过交叉算子和突变算子的更好组合。结果,本文研究了可以通过实验设计(DOE)用于此目的的遗传算子。本文提出了合适的遗传算子,可将其应用于遗传算法中,该遗传算法采用两部分染色体编码技术。

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