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Hybrid evolutionary algorithm for the Capacitated Centered Clustering Problem

机译:容量中心聚类问题的混合进化算法

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The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS.
机译:容量集中式聚类问题(CCCP)包括在n个点的网络上定义一组具有最小相似性的p组。需求值与每个点关联,并且每个组都有一个需求容量。众所周知,该问题是NP难题,并且具有许多实际应用。本文采用混合方法聚类搜索(CS)来解决CCCP问题。该方法通过使用元启发式算法(例如遗传算法)生成解决方案,并将其聚类为簇,然后使用本地搜索启发式进行进一步探索,从而确定搜索空间中有希望的区域。提出了考虑文献中可用实例的计算结果,以证明CS的有效性。

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