Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization
Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization
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DOI：
10.1007/s00500-010-0617-8
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发表时间：
2011-09
期刊：
影响因子：
4.1
通讯作者：
H. Ishibuchi;Yuji Sakane;Noritaka Tsukamoto;Y. Nojima
中科院分区：
文献类型：
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作者：
H. Ishibuchi;Yuji Sakane;Noritaka Tsukamoto;Y. Nojima
在元胞算法中，通常假定用于局部选择的单个邻域结构为每个元胞指定一组邻域。然而，在自然界中存在着一些具有两种邻域结构的例子。一种是为了交配而进行的局部选择，另一种是为了在邻近植物之间争夺水和阳光等局部竞争。本文的目的是展示具有两个邻域结构的元胞算法在单目标和多目标优化问题上的几种实现。由于局部选择已经在文献中应用于元胞算法中，因此本文的主要问题是如何实现局部竞争的概念。我们展示了关于其利用的三种观念：地方精英主义、地方排名和地方替代。采用局部精英主义和局部排序进行单目标优化，增加解的多样性。另一方面，采用局部替换进行多目标优化，提高了Pareto边界解的收敛性。我们的方法的主要特点是两个邻域结构可以彼此独立地指定。因此，我们可以单独检查每个邻域结构对细胞算法行为的影响。
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.