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A modified VNS metaheuristic for max-bisection problems

机译:修正的VNS元启发式用于最大二等分问题

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摘要

Variable neighborhood search (VNS) metaheuristic as presented in Festa et al. [Randomized heuristics for the MAX-CUT problem, Optim. Methods Software 17 (2002) 1033-1058] can obtain high quality solution for max-cut problems. Therefore, it is worthwhile that VNS metaheuristic is extended to solve max-bisection problems. Unfortunately, comparing with max-cut problems, max-bisection problems have more complicated feasible region via the linear constraint e(T)x = 0. It is hard to directly apply the typical VNS metaheuristic to deal with max-bisection problems. In this paper, we skillfully combine the constraint e(T)X = 0 with the objective function, obtain a new optimization problem which is equivalent to the max-bisection problem, and then adopt a distinct greedy local search technique to the resulted problem. A modified VNS metaheuristic based on the greedy local search technique is applied to solve max-bisection problems. Numerical results indicate that the proposed method is efficient and can obtain high equality solution for max-bisection problems.
机译:如Festa等人所述,可变邻域搜索(VNS)元启发式。 [针对MAX-CUT问题的优化启发式算法Optim。方法软件17(2002)1033-1058]可以针对最大割问题获得高质量的解决方案。因此,值得将VNS元启发式方法扩展为解决最大二分法问题。不幸的是,与最大割问题相比,通过线性约束e(T)x = 0,最大对分问题具有更复杂的可行区域。很难直接应用典型的VNS元启发式方法来处理最大对分问题。在本文中,我们巧妙地将约束e(T)X = 0与目标函数结合起来,获得了一个与最大二等分问题等效的新优化问题,然后对所得问题采用独特的贪婪局部搜索技术。提出了一种基于贪婪局部搜索技术的改进的VNS元启发式算法来解决最大二分法问题。数值结果表明,该方法是有效的,并且可以解决最大二分法问题。

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