Use of biased neighborhood structures in multiobjective memetic algorithms
Use of biased neighborhood structures in multiobjective memetic algorithms
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DOI：
10.1007/s00500-008-0352-6
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发表时间：
2009-03
期刊：
影响因子：
4.1
通讯作者：
H. Ishibuchi;Y. Hitotsuyanagi;Noritaka Tsukamoto;Y. Nojima
中科院分区：
文献类型：
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作者：
H. Ishibuchi;Y. Hitotsuyanagi;Noritaka Tsukamoto;Y. Nojima
在本文中，我们研究了使用偏置邻域结构的多目标模因算法的局部搜索。在有偏邻域结构下，当前解的每个邻域在局部搜索中被采样的概率不同。在标准的局部搜索中，当前解的所有邻居通常具有相同的概率，因为它们是随机采样的。另一方面，为了提高多目标模因算法的搜索能力，我们将更大的概率分配给更有希望的邻居。在本文中，我们首先解释我们的多目标模因算法，这是一个简单的混合算法NSGA-II和本地搜索。然后，我们解释了它的变种有偏的邻域结构的多目标0/1背包和流水车间调度问题。最后，我们通过计算实验来检查每个变体的性能。实验结果表明，使用偏置邻域结构明显提高了我们的多目标模因算法的性能。
In this paper, we examine the use of biased neighborhood structures for local search in multiobjective memetic algorithms. Under a biased neighborhood structure, each neighbor of the current solution has a different probability to be sampled in local search. In standard local search, all neighbors of the current solution usually have the same probability because they are randomly sampled. On the other hand, we assign larger probabilities to more promising neighbors in order to improve the search ability of multiobjective memetic algorithms. In this paper, we first explain our multiobjective memetic algorithm, which is a simple hybrid algorithm of NSGA-II and local search. Then we explain its variants with biased neighborhood structures for multiobjective 0/1 knapsack and flowshop scheduling problems. Finally we examine the performance of each variant through computational experiments. Experimental results show that the use of biased neighborhood structures clearly improves the performance of our multiobjective memetic algorithm.