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首页> 外文期刊>Journal of Theoretical and Applied Information Technology >WHALE OPTIMIZATION ALGORITHM FOR SOLVING THE MAXIMUM FLOW PROBLEM
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WHALE OPTIMIZATION ALGORITHM FOR SOLVING THE MAXIMUM FLOW PROBLEM

机译:鲸鱼优化算法解决最大流量问题

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Maximum Flow Problem (MFP) is deemed as one of several well-known basic problems in weighted direct graphs [9]. Moreover, it can be applied to many applications in computer engineering and computer science. This problem is solved by many techniques. Thus, this study presents a possible solution to the max flow problem (MFP) using a Whale Optimization algorithm (WOA), which is considered as one of the most recent optimization algorithms that was suggested in 2016. The experimental results of the ?MaxFlow-WO? algorithm that were tested on various datasets are good evidence that the s technique can solve the MFP and reinforce its performance. It aims to solve the MFP by clustering the search space to find the MF for each cluster (local MF) in order to find the overall solution (global MF) of the desired graph. The WOA is used to solve the MFP by supposing the graph is the search space that the whales looking to reach the prey. Here, the prey is the sink in the network (represented by the graph) and other whales are represented in other nodes in the graph.
机译:最大流量问题(MFP)被认为是加权直接图中的几个众所周知的基本问题之一[9]。此外,它可以应用于计算机工程和计算机科学的许多应用。这个问题是通过许多技术解决的问题。因此,该研究呈现了使用鲸鱼优化算法(WOA)的最大流量问题(MFP)的可能解决方案,该方法被认为是2016年建议的最新优化算法之一。MaxFlow-的实验结果窝?在各种数据集上测试的算法是好的证据表明,S技术可以解决MFP并加强其性能。它旨在通过群集搜索空间来解决每个群集(本地MF)的MF来解决MFP,以便找到所需图的整体解决方案(全局MF)。 WOA用于通过假设图来解决MFP是鲸鱼希望到达猎物的搜索空间。这里,猎物是网络中的水槽(由图表表示),而其他鲸可以在图中的其他节点中表示。

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