Multicriteria Network Design Using Evolutionary Algorithm

Kumar, Rajeev; Banerjee, Nilanjan

doi:10.1007/3-540-45110-2_113

Cited by 16 publications

(9 citation statements)

References 23 publications

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“…There are many application areas of multi-objective genetic algorithms (MOGA) in the literature such as flowshop scheduling [3,30], telecommunication systems [21,22], reliability optimization [14,46], finance, covering tour problem, Aerodynamic design, fluid power systems, manufacturing systems, DNA sequences, attribute selection [12], and clustering [31][32][33].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

2009

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Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

2009

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“…In [2], [7], and [14], Evolutionary Algorithms are used to search for Pareto-optimal multi-cast routings for a given topology. Hence, all these approaches use a genetic encoding of the routing-paths for a fixed topology Problem Graph Architecture Graph including resources and communication connections.…”

Section: Related Workmentioning

confidence: 99%

“…As already mentioned, the approach proposed in this work has to deal with changing topologies and bindings from the first two decoding steps. Using the encodings proposed in [2], [7], [14], a change of the topology would imply a new encoding of the routing and therefore, a loss of all information for the routing optimization.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective routing and topology optimization in networked embedded systems

Glaß

Lukasiewycz

Wanka

et al. 2008

2008 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation

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Abstract-Modern networked embedded system design has to cope with multiple design objectives. One major challenge is the determination of optimal routings with respect to these objectives. Existing automatic optimization approaches carry out a two step optimization: First, they perform a multi-objective topology optimization of the networked embedded system. Then, a multi-objective routing optimization for a subset of Paretooptimal solutions obtained from the first step is performed. In general, this may exclude several globally optimal solutions from the optimization process. To overcome this drawback, a unified approach based on Multi-Objective Evolutionary Algorithms is presented that ensures a combined optimization of the topology and routing. Since the system topology is varied within the optimization, the main contribution of this paper contribution is a novel routing technique that always samples feasible paths using a topology independent genetic encoding. This encoding preserves optimized routing information when changing the underlying topology. An experimental evaluation shows the effectiveness of the presented approach.

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“…We presented few initial results of network topology design for Poisson traffic model in [17,18]. In this work, we present a framework using EAs that simultaneously optimize multiple objectives and produces a set of non-dominated equivalent solutions that lie on (near-) optimal Pareto front.…”

Section: Communication Network Design -A Reviewmentioning

confidence: 99%

Multiobjective network design for realistic traffic models

Banerjee

Kumar

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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Network topology design problems find application in several real life scenarios. However, most designs in the past either optimize for a single criterion like delay or assume simplistic traffic models like Poisson. Such assumptions make the solutions inapplicable in the practical world.In this paper, we formulate and solve a multiobjective network topology design problem for a realistic Internet traffic model which is assumed to be self similar. We optimize for the average packet delivery delay and network layout cost to construct realistic network topologies. We present a multiobjective evolutionary algorithm (MOEA) to obtain the diverse near-optimal network topologies. For fair comparison, we design a multiobjective deterministic heuristic based on branch exchange -we call the heuristic Pareto Branch Exchange (PBE). We empirically show that the MOEA used performs well for real networks of various sizes, and generated topologies are quite different with significantly larger delays for the self similar traffic model.

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Multicriteria Network Design Using Evolutionary Algorithm

Cited by 16 publications

References 23 publications

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Multi-objective routing and topology optimization in networked embedded systems

Multiobjective network design for realistic traffic models

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