A QoS routing method for ad-hoc networks based on genetic algorithm

Barolli, Leonard; Kôyama, Akio; Shiratori, Norio

doi:10.1109/dexa.2003.1232019

Cited by 47 publications

(34 citation statements)

References 10 publications

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“…In order to solve large-scale QoS optimization problems, it is required to use heuristic algorithms such as GAs [7][8][9][10][11]. In general, GAs scale better than linear optimization algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…High dimensionality in the objective space often leads to premature convergence, which fails to improve the optimization quality (or optimality) of QoS parameter sets. Traditional QoS optimization algorithms tend to deal with a limited number of parameters and optimization objectives; for example, less than 20 QoS parameters and three or less optimization objectives 1 [3][4][5][6][7][8][9][10][11][15][16][17][18][19][20][21][22].…”

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confidence: 99%

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Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks

2011

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This paper proposes and evaluates an evolutionary multiobjective optimization algorithm, called EVOLT, which heuristically optimizes QoS (quality of service) parameters in communication networks. EVOLT uses a population of individuals, each of which represents a set of QoS parameters, and evolves the individuals via genetic operators such as crossover, mutation and selection for satisfying given QoS requirements. For evaluating EVOLT in real-world settings that have high-dimensional parameter and optimization objective spaces, this paper focuses on QoS optimization in safety-critical communication networks for electric power utilities. Simulation results show that EVOLT outperforms a well-known existing evolutionary algorithm for multiobjective optimization and efficiently obtains quality QoS parameters with acceptable computational costs. Moreover, EVOLT visualizes obtained QoS parameters in a Self-Organizing Map in order to aid network administrators to intuitively understand the QoS parameters and the tradeoffs among optimization objectives.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks

2011

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“…A GA-based routing method for MANET (GAMAN) [17] was proposed to find a feasible path from multiple paths, hence providing robustness, and it is a source-routing protocol. E-GAMAN [18] is an enhanced version of GAMAN with the addition of an effective topology extraction algorithm to reduce the search space of GAMAN.…”

Section: Ga-based Qos Routing In Manetsmentioning

confidence: 99%

Energy efficient multiconstrained optimization using hybrid ACO and GA in MANET routing

Kumaran¹,

Asokan²

2016

Turk J Elec Eng & Comp Sci

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Nodes in mobile ad hoc networks (MANET) suffer from limited battery power and bandwidth. Particularly for real time multimedia communications through MANET, metrics like residual node energy, bandwidth, and end-toend delay have major impacts. In MANET, designing a dynamic routing algorithm to satisfy quality of service (QoS) requirements is a challenging task. Additionally, multiconstrained QoS routing aims to optimize multiple QoS metrics while providing required network resources and is an admittedly complex problem. It has been proved to be NP-complete when a combination of additive, concave, and multiplicative metrics are considered. Hence, this problem can be solved using metaheuristic methods like ant colony optimization (ACO) and the genetic algorithm (GA). The proposed energyefficient ACO GA hybrid metaheuristic approach aims to utilize the benefits of both as a combined approach in order to reduce the routing complexities in the dynamic environment. After due investigation, it has been shown that the proposed hybrid approach improves the performance of MANET routing with satisfied QoS requirements.

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“…It has been proved that such a problem is NP-complete. QMRPD (QoS Multicast Routing Protocol for Dynamic group topology) [33] GAMAN (Genetic Algorithm-based routing for MANETs) [34] H-MCOP (Heuristic multi Constrained Optimal Path) are typical multi constrained routing protocols.…”

Section: Single Constrained Vs Multi Constrained Qos Metricsmentioning

confidence: 99%