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Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization

机译:具有两个邻域结构的单目标和多目标优化的细胞遗传算法的实现

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In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms.
机译:在蜂窝算法中,通常假定用于局部选择的单个邻域结构为每个小区指定一组邻居。但是,自然界中存在许多具有两个邻域结构的示例。一种是在本地进行交配选择,另一种是在本地竞争,例如在邻近植物之间争夺水和阳光。本文的目的是展示针对单目标和多目标优化问题的具有两个邻域结构的元胞算法的几种实现。由于文献中已经将局部选择应用于细胞算法中,因此本文的主要问题是如何实现局部竞争的概念。我们展示了有关其利用的三个想法:地方精英,地方等级和地方替代。局部精英和局部排名用于单目标优化,以增加解决方案的多样性。另一方面,将局部替换用于多目标优化,以提高对Pareto边界的解决方案的收敛性。我们方法的主要特征是两个邻域结构可以彼此独立地指定。因此,我们可以分别检查每个邻域结构对细胞算法行为的影响。

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