Abstract
In this paper, based on the simplified swarm optimization (SSO) algorithm and genetic algorithm (GA), a novel GA-SSO hybrid optimization algorithm is proposed to solve the NP-hard channel assignment problem of multi-radio multi-channel (MR-MC) wireless sensors networks (WSN) which is promising for data intensive application. The aim of channel assignment is to minimize total network interference so as to maximize network throughput. In the GA-SSO based channel assignment, an improved channel merging method is proposed to satisfy the interface constraint condition. Matlab based simulation results show that the proposed GA-SSO features better global search capacity and can reduce the total network interference more effectively, compared to the SSO and DPSO-CA algorithms.
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Acknowledgement
The authors would like to acknowledge the supports by the National Natural Science Foundation of China (Grant Nos. 61601127, 51508105, and 61574038), the Fujian Provincial Department of Science and Technology of China (Grant Nos. 2015H0021, 2015J05124 and 2016H6012), the Fujian Provincial Economic and Information Technology Commission of China (Grant No. 830020) and the Fujian Provincial Department of Education of China (Grant No. JA14038).
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Xu, Z., Chen, Z., Wu, L., Lin, P., Cheng, S. (2016). A GA-SSO Based Intelligent Channel Assignment Approach for MR-MC Wireless Sensors Networks. In: Sombattheera, C., Stolzenburg, F., Lin, F., Nayak, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. Lecture Notes in Computer Science(), vol 10053. Springer, Cham. https://doi.org/10.1007/978-3-319-49397-8_27
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DOI: https://doi.org/10.1007/978-3-319-49397-8_27
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