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Tight oscillations tabu search for multidimensional knapsack problems with generalized upper bound constraints

机译:紧振动禁忌搜索的广义上界约束多维背包问题

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In a recent paper, the author and Curry solved the multidimensional knapsack problem with generalized upper bound constraints by a critical-event tabu-search method which navigates both sides of the feasibility boundary with varied depth of oscillations. Efforts were made to explore the solution space near the feasibility boundary by using local swaps according to the objective function values (the resulting solutions are referred to as simple trial solutions). In this paper, a specialized tight-oscillation process is launched to intensify the search when the previous method finds good solutions or simple trial solutions near the feasibility boundary. Both feasibility changes and objective-function value changes are incorporated into the choice of moves process. This paper demonstrates the merits of using different choice rules at different stages of the heuristic. The balance of intensification and diversification is achieved by using two levels of strategic oscillation approaches together with tabu memory at the main heuristic stage and the trial solution stage. With the tight oscillation method, the heuristic is able to find high-quality solutions very efficiently.
机译:在最近的一篇论文中,作者和库里通过临界事件禁忌搜索方法解决了具有广义上界约束的多维背包问题,该方法在振荡深度可变的情况下导航了可行性边界的两侧。努力根据目标函数值使用局部交换来探索可行性边界附近的解决方案空间(所得解决方案称为简单试验解决方案)。在本文中,当前一种方法在可行性边界附近找到好的解决方案或简单的试验解决方案时,将启动专门的紧密振荡过程,以加强搜索。可行性更改和目标函数值更改都被合并到移动过程的选择中。本文展示了在启发式的不同阶段使用不同选择规则的优点。通过在主要启发式阶段和试行解决阶段使用两个级别的策略振荡方法以及禁忌记忆来实现强化和多样化之间的平衡。使用紧密振荡方法,启发式方法可以非常有效地找到高质量的解决方案。

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