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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Evolutionary and Principled Search Strategies for Sensornet Protocol Optimization
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Evolutionary and Principled Search Strategies for Sensornet Protocol Optimization

机译：Sensornet协议优化的进化和有原则的搜索策略

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摘要

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.
机译：多个可调协议参数和多个性能指标之间的交互通常是复杂且未知的。通常很难找到最佳解决方案。但是，协议调整可以显着提高能源效率和资源需求，这对于资源可用性受到严格限制的Sensornet系统尤为重要。我们针对两种不同的路由协议并通过两种不同的方法解决了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排除无关紧要的因素，并通过有原则的搜索来定位包含接近最优解的问题空间的区域。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，每次迭代都可能产生比先前迭代更高质量和多样性的解决方案。原则上的搜索方法可以对问题空间进行普遍适用的调查，并可以进行性能预测，而进化方法则可以以较低的实验成本提供更高质量的可行解决方案。这是第一项将传感器网络协议优化明确定义为多目标问题并使用最新的多目标进化算法解决的研究。

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