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Frequency Model Based Crossover Operators for Genetic Algorithms Applied to the Quadratic Assignment Problem

机译:基于频率模型的遗传算法交叉算子应用于二次分配问题

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The quadratic assignment problem aims to find an optimal assignment of a set of facilities to a set of locations that minimizes an objective function depending on the flow between facilities and the distance between locations. In this paper we investigate Genetic Algorithm (GA) using new crossover operators to guide the search towards unexplored regions of the search space. First, we define a frequency model which keeps in memory a frequency value for each pair of facility and location. Then, relying on the frequency model we propose three new crossover operators to enhance genetic algorithm for the quadratic assignment problem. The first and second ones, called Highest Frequency crossover (HFX) and Greedy HFX (GHFX), are based only on the frequency values, while the third one, called Highest Frequency Minimum Cost crossover (HFMCX), combines the frequency values with the cost induced by assigning facilities to locations. The experimental results comparing the proposed crossover operators to three efficient crossover operators, namely, One Point crossover (OPX), Swap Path crossover (SPX) and Sequential Constructive crossover (SCX), show effectiveness of our proposed operators both in terms of solution quality and computational time.
机译:二次分配问题旨在找到一组设施对一组位置的最佳分配,从而根据设施之间的流量和位置之间的距离使目标函数最小化。在本文中,我们研究了使用新交叉算子的遗传算法(GA),以将搜索引导至搜索空间的未探索区域。首先,我们定义一个频率模型,该模型在内存中为每对设施和位置保持一个频率值。然后,根据频率模型,我们提出了三个新的交叉算子,以增强遗传算法求解二次分配问题。第一个和第二个称为最高频率交叉(HFX)和贪婪的HFX(GHFX),仅基于频率值,而第三个称为最高频率最小成本交叉(HFMCX),将频率值与成本相结合通过将设施分配给位置而引起。实验结果将建议的算子与三种有效的算子进行了比较,这三个算子是单点算子(OPX),交换路径算子(SPX)和顺序构造算子(SCX),显示了我们的算子在解决方案质量和解决方案质量方面均有效。计算时间。

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