A multiobjective genetic algorithm for radio network optimization

Meunier, Hervé; Talbi, El‐Ghazali; Reininger, Philippe

doi:10.1109/cec.2000.870312

Cited by 94 publications

(77 citation statements)

References 7 publications

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“…They seek the optimal tradeoff (or Pareto-optimal) solutions and have no weight parameters to manually configure in their fitness functions [15][16][17][18]. Unlike existing MOGAs, EVOLT is designed to handle high-dimensional parameter and objective spaces well, minimize the number of manually-configured constants in genetic operators and visualize non-dominated individuals in a low-dimensional (two dimensional) SOM space.…”

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%

mentioning

confidence: 99%

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

2011

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“…Thus, the hypervolume metric is available both in its unary [51] and its binary [52] form. Moreover, the entropy metric [5], the contribution metric [35] as well as the additive and the multiplicative ε-indicators [52] are all implemented and can thus be used to compare two sets of solutions. Besides, some implementations for pairwise comparison of solutions (that are then usable within the binary indicator-based fitness assignment schemes, see Sect.…”

Section: Stopping Criteria Checkpointing and Statistical Toolsmentioning

confidence: 99%

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

Liefooghe

Jourdan

Legrand

et al. 2010

Advances in Multi-Objective Nature Inspired Computing

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This chapter presents ParadisEO-MOEO, a white-box object-oriented software framework dedicated to the flexible design of metaheuristics for multi-objective optimization. This paradigm-free software proposes a unified view for major evolutionary multi-objective metaheuristics. It embeds some features and techniques for multi-objective resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the problems they are intended to solve. This separation confers a maximum design and code reuse. This general-purpose framework provides a broad range of fitness assignment strategies, the most common diversity preservation mechanisms, some elitistrelated features as well as statistical tools. Furthermore, a number of state-of-the-art search methods, including NSGA-II, SPEA2 and IBEA, have been implemented in a user-friendly way, based on the fine-grained ParadisEO-MOEO components

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“…Alternatively in the fixed-structure genotype camp, a genotype would One noticeable exception to this is the "multi-level encoding" in Meunier at al. [9]. In their model, the BS site activation, antenna type selection, and antenna configuration are encoded as three levels.…”

Section: Individual Representationmentioning

confidence: 99%

WiMAX Network Planning Using Adaptive-Population-Size Genetic Algorithm

Chen

Banzhaf

2010

Applications of Evolutionary Computation

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Abstract. IEEE 802.16, also known as WiMAX, is a new wireless access technology for currently increasing demand of wireless high-speed broadband service. Efficient and effective deployment of such a network to service an area of users with certain traffic demands is an important network planning problem. In this article, we resort to a Genetic Algorithm in order to yield good approximation solutions. In our method, individual representation and genetic variation operations are specifically designed to incorporate the feature of this application problem. Moreover, an adaptive population size approach inspired by neutral theory in molecular biology is applied in our algorithm to enhance its search ability. Simulation results show that our algorithm is fairly effective and robust to different scenarios of the network planning problem. By comparing to a conventional fixed population size scheme, our method is further verified to be able to accelerate the search process.

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A multiobjective genetic algorithm for radio network optimization

Cited by 94 publications

References 7 publications

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

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

ParadisEO-MOEO: A Software Framework for Evolutionary Multi-Objective Optimization

WiMAX Network Planning Using Adaptive-Population-Size Genetic Algorithm

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