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A parallel algorithm with enhancements via partial objective value cuts for cluster-based wireless sensor network design

机译:一种基于局部目标值削减的增强型并行算法,用于基于集群的无线传感器网络设计

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In this paper, we develop a parallel algorithm for the solution of an integrated topology control and routing problem in Wireless Sensor Networks (WSNs). After presenting a mixed-integer linear optimization formulation for the problem, for its solution, we develop an effective parallel algorithm in a Master-Worker model that incorporates three parallelization strategies, namely low-level parallelism, domain decomposition, and multiple search (both cooperative and independent) in a single Master-Worker framework. For improved algorithmic efficiency, we introduce three reduced subproblems and devise partial objective value cuts from these reduced models. We utilize both the reduced models, for which we suggest efficient approaches for their solution, and the associated cuts in our parallel algorithm. We observe that the reduced models provide valuable information on the optimal design variables for the original model and we exploit this fact in our parallel algorithm. Our overall parallelization scheme utilizes exact optimization models and solutions as its components and allows cooperation among multiple worker processors via communication of partial solution and cut information. Computational study shows that our approach is very effective in addressing this complex problem. Parallel implementation not only achieves a speed-up of the computations, but also yields better solutions as it can explore the solution space more effectively.
机译:在本文中,我们开发了一种并行算法来解决无线传感器网络(WSN)中的集成拓扑控制和路由问题。在提出了该问题的混合整数线性优化公式后,为解决该问题,我们在Master-Worker模型中开发了一种有效的并行算法,该模型结合了三种并行化策略,即低级并行性,域分解和多重搜索(两者都是协作的)并且独立)在一个Master-Worker框架中。为了提高算法效率,我们引入了三个简化的子问题,并根据这些简化的模型设计了部分目标价值削减方法。我们既利用简化模型(我们为其提出了有效的解决方案),又利用并行算法中的相关削减。我们观察到简化后的模型为原始模型的最佳设计变量提供了有价值的信息,并且我们在并行算法中利用了这一事实。我们的整体并行化方案利用精确的优化模型和解决方案作为其组件,并允许通过部分解决方案和切割信息的通信在多个工作处理器之间进行协作。计算研究表明,我们的方法在解决这一复杂问题方面非常有效。并行实现不仅可以加快计算速度,而且还可以提供更好的解决方案,因为它可以更有效地探索解决方案空间。

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