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Bee colony optimization for the p-center problem

机译:针对p中心问题的蜂群优化

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Bee colony optimization (BCO) is a relatively new meta-heuristic designed to deal with hard combinatorial optimization problems. It is biologically inspired method that explores collective intelligence applied by the honey bees during nectar collecting process. In this paper we apply BCO to the p-center problem in the case of symmetric distance matrix. On the contrary to the constructive variant of the BCO algorithm used in recent literature, we propose variant of BCO based on the improvement concept (BCOi). The BCOi has not been significantly used in the relevant BCO literature so far. In this paper it is proved that BCOi can be a very useful concept for solving difficult combinatorial problems. The numerical experiments performed on well-known benchmark problems show that the BCOi is competitive with other methods and it can generate high-quality solutions within negligible CPU times.
机译:蜂群优化(BCO)是一种相对较新的元启发式方法,旨在解决组合优化难题。这是一种受生物启发的方法,可以探索蜜蜂在花蜜采集过程中所应用的集体智慧。在本文中,我们将BCO应用于对称距离矩阵情况下的p中心问题。与最近文献中使用的BCO算法的建设性变体相反,我们基于改进概念(BCOi)提出了BCO变体。到目前为止,BCOi在相关的BCO文献中尚未得到广泛使用。本文证明了BCOi对于解决组合难题非常有用。针对众所周知的基准问题进行的数值实验表明,BCOi与其他方法相比具有竞争优势,并且可以在可忽略的CPU时间内生成高质量的解决方案。

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