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Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks

Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks

Debashis Dev Misra, Kandarpa Kumar Sarma, Pradyut Kumar Goswami, Subhrajyoti Bordoloi, Utpal Bhattacharjee
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 22
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781683180449|DOI: 10.4018/IJMCMC.297962
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MLA

Misra, Debashis Dev, et al. "Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks." IJMCMC vol.13, no.1 2022: pp.1-22. http://doi.org/10.4018/IJMCMC.297962

APA

Misra, D. D., Sarma, K. K., Goswami, P. K., Bordoloi, S., & Bhattacharjee, U. (2022). Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 13(1), 1-22. http://doi.org/10.4018/IJMCMC.297962

Chicago

Misra, Debashis Dev, et al. "Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 13, no.1: 1-22. http://doi.org/10.4018/IJMCMC.297962

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Abstract

Routing mechanisms in Ultra-Dense Network (UDNs) are expected to be flexible, scalable, and robust in nature and the establishment of the shortest path between the source and destination pairs will always be a critical challenge. Through this projected work, the optimized shortest route of different source-destination pairs is found using a class of evolutionary optimization algorithms namely PSO, GA, and our proposed hybrid PSO–Genetic Mutation (PSO-GM) algorithm which searches for an optimized solution by representing it as a Shortest Path Routing (SPR) problem. The key attribute of the PSO-GM approach is related to the application of an amalgamated strategy with Gaussian, Cauchy, Levy, Single-point, and Chaos mutation operators. Simulation results and application of the above-mentioned algorithms to the SPR problem in UDNs reveal that the hybrid PSO-GM algorithm provides a comparatively enhanced optimized solution. In the case of the rate of convergence to the theoretical limit, the hybrid PSO-GM gives us 20% better results compared to the PSO and GA.