Tate Jonathan, Woolford-Lim Benjamin, Bate Iain, Yao Xin
Department of Computer Science, University of York, YO10 5DD York, U.K.
IEEE Trans Syst Man Cybern B Cybern. 2012 Feb;42(1):163-80. doi: 10.1109/TSMCB.2011.2161466. Epub 2011 Aug 18.
Interactions between multiple tunable protocol parameters and multiple performance metrics are generally complex and unknown; finding optimal solutions is generally difficult. However, protocol tuning can yield significant gains in energy efficiency and resource requirements, which is of particular importance for sensornet systems in which resource availability is severely restricted. We address this multi-objective optimization problem for two dissimilar routing protocols and by two distinct approaches. First, we apply factorial design and statistical model fitting methods to reject insignificant factors and locate regions of the problem space containing near-optimal solutions by principled search. Second, we apply the Strength Pareto Evolutionary Algorithm 2 and Two-Archive evolutionary algorithms to explore the problem space, with each iteration potentially yielding solutions of higher quality and diversity than the preceding iteration. Whereas a principled search methodology yields a generally applicable survey of the problem space and enables performance prediction, the evolutionary approach yields viable solutions of higher quality and at lower experimental cost. This is the first study in which sensornet protocol optimization has been explicitly formulated as a multi-objective problem and solved with state-of-the-art multi-objective evolutionary algorithms.
多个可调协议参数与多个性能指标之间的相互作用通常很复杂且未知；找到最优解通常很困难。然而，协议调优可以在能源效率和资源需求方面带来显著提升，这对于资源可用性受到严重限制的传感器网络系统尤为重要。我们通过两种不同的方法针对两种不同的路由协议解决这个多目标优化问题。首先，我们应用析因设计和统计模型拟合方法来剔除无关因素，并通过有原则的搜索定位问题空间中包含近似最优解的区域。其次，我们应用强度帕累托进化算法2和双存档进化算法来探索问题空间，每次迭代都有可能产生比前一次迭代质量更高、多样性更强的解。虽然有原则的搜索方法能对问题空间进行普遍适用的考察并实现性能预测，但进化方法能以更低的实验成本产生质量更高的可行解。这是第一项将传感器网络协议优化明确表述为多目标问题并用最先进的多目标进化算法解决的研究。
