Posts Tagged ‘NSGA’

Non-dominated Sorting Genetic Algorithm

November 6, 2018

Non-dominated Sorting Genetic Algorithm, Nondominated Sorting Genetic Algorithm, Fast Elitist Non-dominated Sorting Genetic Algorithm, NSGA, NSGA-II

Taxonomy

The Non-dominated Sorting Genetic Algorithm is a Multiple Objective Optimization (MOO) algorithm and is an instance of an Evolutionary Algorithm from the field of Evolutionary Computation. Refer to for more information and references on Multiple Objective Optimization. NSGA is an extension of the Genetic Algorithm for multiple objective function optimization. It is related to other Evolutionary Multiple Objective Optimization Algorithms (EMOO) (or Multiple Objective Evolutionary Algorithms MOEA) such as the Vector-Evaluated Genetic Algorithm (VEGA), Strength Pareto Evolutionary Algorithm (SPEA), and Pareto Archived Evolution Strategy (PAES). There are two versions of the algorithm, the classical NSGA and the updated and currently canonical form NSGA-II.

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Evolutionary Multi-Objective Based Approach for Wireless Sensor Network Deployment

January 14, 2014

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Here is the abstract from our paper that already accepted and will be presented on ICC 2014 (International Conference on Communication) that will be held in the beautiful city of Sydney, Australia from 10-14 June 2014.

Abstract: A multi-objective evolutionary algorithm is designed to address some problems in many fields. This paper is a study about deployment strategy for achieving coverage and connectivity as two fundamental issues in wireless sensor networks. To achieve the best deployment, our approach is based on elitist non-dominated sorting genetic algorithm (NSGA-II). There are two objectives in this study, connectivity and coverage. We defined a fitness function to achieved the best deployment of nodes. Further we performed simulation to verify and validate the deployment of wireless sensor network as an output from our proposed mechanism. We measured some performance parameters to investigate and analyze our proposed sensor-deployment. Our simulation results show that our proposed algorithm can maintain coverage and connectivity in given sensing area with a relatively small number of sensor nodes in a given area.