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首页> 外文期刊>European Journal of Operational Research >Tabu-enhanced iterated greedy algorithm: A case study in the quadratic multiple knapsack problem
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Tabu-enhanced iterated greedy algorithm: A case study in the quadratic multiple knapsack problem

机译：禁忌增强迭代贪婪算法：二次多重背包问题的案例研究

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摘要

Iterated greedy search is a simple and effective metaheuristic for combinatorial problems. Its flexibility enables the incorporation of components from other metaheuristics with the aim of obtaining effective and powerful hybrid approaches. We propose a tabu-enhanced destruction mechanism for iterated greedy search that records the last removed objects and avoids removing them again in subsequent iterations. The aim is to provide a more diversified and successful search process with regards to the standard destruction mechanism, which selects the solution components for removal completely at random. We have considered the quadratic multiple knapsack problem as the application domain, for which we also propose a novel local search procedure, and have developed experiments in order to assess the benefits of the proposal. The results show that the tabu-enhanced iterated greedy approach, in conjunction with the new local search operator, effectively exploits the problem-knowledge associated with the requirements of the problem considered, attaining competitive results with regard to the corresponding state-of-the-art algorithms.
机译：迭代贪婪搜索是组合问题的一种简单有效的元启发式方法。它的灵活性使得可以合并其他元启发式方法中的组件，以期获得有效而强大的混合方法。我们为迭代贪婪搜索提出了一种禁忌增强的破坏机制，该机制记录了最后删除的对象，并避免了在后续迭代中再次删除它们。目的是针对标准销毁机制提供一种更加多样化和成功的搜索过程，该过程可以随机选择要完全删除的解决方案组件。我们已经考虑了二次多重背包问题作为应用领域，为此，我们还提出了一种新颖的局部搜索程序，并进行了实验以评估该提议的好处。结果表明，禁忌增强的迭代贪婪方法与新的本地搜索运算符一起，有效地利用了与所考虑问题的需求相关的问题知识，从而在相应的现状下获得了竞争性的结果。艺术算法。

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