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首页> 外文期刊>Mathematical Problems in Engineering >Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas
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Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas

机译：边界区域上无线自组织传感器分布的多目标优化

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摘要

Resource efficiency in wireless ad hoc networks has become a widely studied NP-problem. This problem may be suboptimally solved by heuristic strategies, focusing on several features like the channel capacity, coverage area, and more. In this work, maximizing coverage area and minimizing energy consumption are suboptimally adjusted with the implementation of two of Storn/Price's Multi objective Differential Evolution (DE) algorithm versions. Additionally, their extended representations with the use of random-M parameter into the mutation operator were also evaluated. These versions optimize the initial random distribution of the nodes in different shaped areas, by keeping the connectivity of all the network nodes by using the Prim-Dijkstra algorithm. Moreover, the Hungarian algorithm is applied to find the minimum path distance between the initial and final node positions in order to arrange them at the end of the DE algorithm. A case base is analyzed theoretically to check how DE is able to find suboptimal solutions with certain accuracy. The results here computed show that the inclusion of random-M and completion of the algorithm, where the area is pondered with 60% and the energy is pondered with 40%, lead to energy optimization and a total coverage area higher than 90%, by considering the best results on each scenario. Thus, this work shows that the aforementioned strategies are feasible to be applied on this problem with successful results. Finally, these results are compared against two typical bioinspired multiobjective algorithms, where the DE algorithm shows the best tradeoff.
机译：无线自组织网络中的资源效率已成为人们广泛研究的NP问题。该问题可能会通过启发式策略得到最佳解决，重点放在信道容量，覆盖范围等多个功能上。在这项工作中，使用Storn / Price的两个多目标差分进化（DE）算法版本的实现对优化覆盖范围和最小化能耗进行了次优调整。另外，还评估了他们在突变算子中使用random-M参数的扩展表示。这些版本通过使用Prim-Dijkstra算法保持所有网络节点的连通性，优化了不同形状区域中节点的初始随机分布。此外，匈牙利算法用于查找初始节点位置和最终节点位置之间的最小路径距离，以便将它们布置在DE算法的末尾。从理论上对案例库进行了分析，以检查DE如何能够找到具有一定准确性的次优解决方案。此处计算出的结果表明，将random-M包括在内并完成了算法，其中面积被考虑为60％，能量被考虑为40％，从而导致能量优化和总覆盖面积大于90％，考虑每种情况下的最佳结果。因此，这项工作表明上述策略可行地应用于该问题并取得了成功的结果。最后，将这些结果与两种典型的生物启发式多目标算法进行比较，其中DE算法表现出最佳的权衡。

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