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Comparison between hybridized algorithm of GA–SA and ABC, GA, DE and PSO for vertical-handover in heterogeneous wireless networks

Goudarzi, S. and Hassan, W. H. and Anisi, M. H. and Soleymani, S. A. (2016) Comparison between hybridized algorithm of GA–SA and ABC, GA, DE and PSO for vertical-handover in heterogeneous wireless networks. Sadhana - Academy Proceedings in Engineering Sciences, 41 (7). pp. 727-753. ISSN 0256-2499

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Abstract

Genetic algorithms (GAs) and simulated annealing (SA) have emerged as leading methods for search and optimization problems in heterogeneous wireless networks. In this paradigm, various access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, the hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid slow and massive computations. This was to, specifically, solve two major problems in GA optimization, i.e. premature convergence and slow convergence rate, and the facilitation of simulated annealing in the merging populations phase of the search. The hybrid algorithm was expected to improve on the pure GA in two ways, i.e., improved solutions for a given number of evaluations, and more stability over many runs. This paper compares the formulation and results of four recent optimization algorithms: artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Moreover, a cost function is used to sustain the desired QoS during the transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR, and monetary cost. Simulation results indicated that choosing the SA rules would minimize the cost function and the GA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.

Item Type:Article
Uncontrolled Keywords:Cost functions, Costs, Distributed computer systems, Evolutionary algorithms, Genetic algorithms, Heterogeneous networks, Mobile devices, Optimization, Particle swarm optimization (PSO), Quality of service, Radio systems, Simulated annealing, Telecommunication links, Wireless networks, Artificial bee colonies (ABC), Fast and efficient algorithms, Genetic algorithm (GAs), Heterogeneous wireless network, Intelligent Algorithms, Optimization algorithms, Pre-mature convergences, Vertical handovers, Algorithms
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:71597
Deposited By: Widya Wahid
Deposited On:20 Nov 2017 08:28
Last Modified:20 Nov 2017 08:28

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