Optimal Base Station Locations in Heterogeneous Wireless Sensor Network Based on Hybrid Particle Swarm Optimization with Bat Algorithm

Tien-Szu Pan, Thi-Kien Dao, Trong-The Nguyen, Shu-Chuan Chu

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    In this paper, a hybrid Particle Swarm Optimization with Bat Algorithm (PBA) for applying to the base-station locations optimization in the heterogeneous wireless sensor networks (WSNs) is proposed. In this work, the several worst individuals of particles in Particle Swarm Optimization (PSO) will be replaced with the best individuals in Bat Algorithm (BA) after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases more the convergence and the accuracy than BA and PSO up to 3% and 47% respectively. In addition, compared with PSO and BA methods, the proposed PBA method can provide the longest the network lifetime of the heterogeneous WSNs.

    Original languageEnglish
    Pages (from-to)14-25
    Number of pages12
    JournalJournal of Computers
    Volume25
    Issue number4
    Publication statusPublished - 2014

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