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Based on differential evolution to research the control problem of area-coverage in WSNs

机译:基于差分演变,研究WSNS中面积覆盖的控制问题

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Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this article, we aim to study the differential evolution and its' variants to research the area-coverage problem of wireless sensor networks (WSNs). Due to the area-coverage problem of WSNs is more important and pragmatic than the point coverage, we introduce a common method to generate test data set for area-coverage problem of WSNs firstly. Meanwhile, we propose a method that converts the area-coverage problem into point disjoint set covers problem. Then using DE and its' variants to solve the disjoint set covers problems. Namely we use DE and its' variants to research the area-coverage problems of WSNs. Finally, simulation comparison experiment are performed for the DE and its' variants. Results show that the JADE (an adaptive differential evolution proposed by Jingqiao and Arthur Sanderson) performance outperforms or same with others algorithms by solution quality, but the proposed variant has greatly better in terms of time complexity and optimization speed. The reason is that there combination operation can enhance the solution quality in early evolution.
机译:差分演进(de)是当前兴趣最强大的随机实际参数优化器之一。在本文中,我们的目标是研究差分演变及其变体,以研究无线传感器网络(WSN)的区域覆盖问题。由于WSN的区域覆盖问题比点覆盖更重要和务实,我们介绍了一个常见的方法,可以首先生成用于区域覆盖问题的测试数据集。同时,我们提出了一种将区域覆盖问题转换为点脱节集的方法。然后使用de及其'变体来解决不相交的集合涵盖问题。即我们使用de及其'变体来研究WSN的区域覆盖问题。最后,对DE及其变体进行仿真比较实验。结果表明,玉(景桥和亚瑟桑德兰提出的自适应差分演进)性能优于其他算法或通过解决方案质量的算法,但是所提出的变体在时间复杂性和优化速度方面具有大大更好。原因是组合操作可以提高早期演化中的解决方案质量。

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