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The Effect of Problem Reduction in the Integer Programming-based Local Search

机译:问题减少了整数基于编程的本地搜索的影响

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Integer Programming-based Local Search (IPbLS) is a kind of local search. IPbLS is based on the first-choice hill-climbing search and uses integer programming to generate a neighbor solution. IPbLS has been applied to solve various NP-hard combinatorial optimization problems like knapsack problem, set covering problem, set partitioning problem, and so on. In this paper, we investigate the effect of problem reduction in the IPbLS experimentally using the n-queens maximization problem. The characteristics of IPbLS are examined by comparing IPbLS using strong problem reduction with IPbLS using weak problem reduction, and also IPbLS is compared with other local search strategies like simulated annealing. Experimental results show the importance of problem reduction in IPbLS.
机译:基于整数的基于编程的本地搜索(IPBLS)是一种本地搜索。 IPBLS基于第一选择山攀爬搜索,并使用整数编程来生成邻居解决方案。 IPBL已经应用于解决像Knapsack问题这样的各种NP硬组合优化问题,设置覆盖问题,设置分区问题等。在本文中,我们使用N-Queens最大化问题在实验上调查IPBLS中的问题减少的影响。通过使用弱问题减少使用弱问题的IPBL对IPBL的强烈问题进行比较来检查IPBL的特征,并且将IPBL与模拟退火等其他本地搜索策略进行比较。实验结果表明,IPBLS问题减少的重要性。

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