Genetic algorithm and pure random search for exosensor distribution optimisation
Genetic algorithm and pure random search for exosensor distribution optimisation
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DOI：
10.1504/ijbic.2012.051408
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发表时间：
2012-01-01
影响因子：
3.5
通讯作者：
Chen, Liming
中科院分区：
文献类型：
--
作者：
Poland, Michael P.;Nugent, Christopher D.;Chen, Liming
外传感器的定位、数量和视场是智能家居环境的基本特征。现代智能家居传感器的分布是对准eithera一个总覆盖的方法或人的评估方法。这些方法的传感器布置不是数据驱动的策略，是非经验的，往往是不合理的。关于智能家居环境中的最佳资源分配的研究很少。本研究的目的是生成全局最优的传感器分布的智能家居复制厨房使用两种不同的方法，即遗传算法（GA）和纯随机搜索算法（PRS），以确定哪种方法是适合这项任务。GA优于PRS一贯，覆盖率的百分比，封装平均43.6%以上的居民空间频率数据。这项研究的结果表明，GA提供了更优化的解决方案比PRS外传感器分布在智能家居环境中。
The positioning, amount(s) and field of view(s) of exosensors are a fundamental characteristic of a smart home environment. Contemporary smart home sensor distribution is aligned to eithera a total coverage approachb a human assessment approach.These methods for sensor arrangement are not data driven strategies, are unempirical, and frequently irrational. Little research has been conducted in relation to optimal resource allocation in smart homes environments. This study aimed to generate globally optimal sensor distributions for a smart home replica-kitchen using two distinct methodologies, namely a genetic algorithm (GA) and a pure random search algorithm (PRS), to ascertain which method is appropriate for this task. GA outperformed PRS consistently, with a coverage percentage that encapsulated an average of 43.6% more inhabitant spatial frequency data. The results of this study indicate that GA provides more optimal solutions than PRS for exosensor distributions in a smart home environment.