Abstract
The computational requirements even in the limited resources of the hardware devices whose small memory size or low price could be addressed by compact optimization methods. In this paper, a compact particle swarm optimization (cPSO) for the base station locations optimization is proposed for wireless sensor networks (WSN). A probabilistic representation random of the collection behavior of swarms is inspired to employ for this proposed algorithm. The real population is replaced with the probability vector updated based on single competition. These lead to the entire algorithm functioning applying a modest memory usage. The experiments to solve the problem of locating the base station in WSN compared with the genetic algorithm (GA) method and the particle swarm optimization (PSO) method show that the proposed method can provide the effective way of using a modest memory.
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Pan, JS., Dao, TK., Nguyen, TT., Pan, TS. (2017). Compact Particle Swarm Optimization for Optimal Location of Base Station in Wireless Sensor Network. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_7
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DOI: https://doi.org/10.1007/978-3-319-48490-7_7
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