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The Coalition Partitioning Strategy Based on the Genetic Algorithm and Markov Random Walk Method

机译:基于遗传算法和马尔可夫随机步行方法的联盟分区策略

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A kind of roadside terminal coalition partitioning strategy based on the improved genetic algorithm is proposed in this paper. This strategy overcomes the strict restrictions that in the Coalition Game Strategy (CGS), the union members need to contribute to all the coalition members in order to join in the coalition and also increases the contribution made by individual member to the specific or part of the coalition, fully considering the influence on the remaining members, The strategy avoids the disadvantage that the coalition partitioning strategy based on graph partition theory relies on the network topology to collaborate so cannot coordinate the cooperative relationship among the members flexibly, thus achieved the local search of the solution space effectively and also improved the average revenue for roadside terminal. The rationality and validity of the strategy were verified by a large number of experiments.
机译:本文提出了一种基于改进遗传算法的路边终端联盟分区策略。该策略克服了在联盟博弈战略(CGS)中的严格限制,联盟成员需要为所有联盟成员捐款,以加入联盟,并提高个人成员对特定或部分的贡献联盟,充分考虑对剩余成员的影响,策略避免了基于图形分区理论的联盟分区策略依赖于合作的网络拓扑结构,因此无法灵活地协调成员之间的合作关系,从而实现了本地搜索解决方案有效,还提高了路边码头的平均收入。通过大量的实验验证了策略的合理性和有效性。

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