Effects of Problem-Specific Local Search Schemes in a Memetic EMO Algorithm
Effects of Problem-Specific Local Search Schemes in a Memetic EMO Algorithm
复制标题
模因 EMO 算法中特定问题局部搜索方案的影响
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
H. Ishibuchi
中科院分区：
文献类型：
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作者：
Y. Hitotsuyanagi;Y. Nojima;H. Ishibuchi
在一些研究中，将局部搜索与进化多目标优化(EMO)算法相结合，以提高EMO算法的搜索能力。EMO算法与局部搜索的混合通常被称为多目标遗传局部搜索(MOGLS)。这种混合算法也被称为多目标模因算法。多目标模因算法比纯EMO算法具有更强的搜索能力。在大多数组合优化问题中，每个问题都有合适的局部搜索方案。这些合适的局部搜索方案可以比遗传搜索高效得多。为了提高搜索效率，我们将特定问题的局部搜索算法发展为一种简单的多目标遗传局部搜索算法(S-MOGLS)。我们提出了适用于多目标背包问题和多目标流跳调度问题的局部搜索方案。通过计算实验，验证了改进的局部搜索方案的有效性。
In some studies, local search has been combined with an evolutionary multiobjective optimization (EMO) algorithm to improve the search ability of the EMO algorithm. Hybridization of an EMO algorithm with local search is often referred to as a multiobjective genetic local search (MOGLS). Such a hybrid algorithm is also called a multiobjective memetic algorithm. Multiobjective memetic algorithms have higher search ability than pure EMO algorithms. In most of combinatorial optimization problems, there exist suitable local search schemes for each problem. These suitable local search schemes can be much more efficient than genetic search. In this paper, to make the search more efficient, we develop the problem-specific local search schemes into a simple multiobjective genetic local search (S-MOGLS) algorithm which we proposed in the previous work. We develop local search schemes suited for multiobjective knapsack problems and multiobjective flows hop scheduling problems. We show the effectiveness of the improved local search schemes through computational experiments.