Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Large-Scale Optimal Sensor Array Management for Multitarget Tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
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Abstract: An energy-efficient Wireless Sensor Network (WSN) design often requires the decision of optimal locations (deployment) and power assignments of the sensors to be densely deployed in an area of interest. In the literature, no attempts have been made on optimizing both decision variables for maximizing the network coverage and lifetime objectives, while maintaining the connectivity constraint, at the same time. In this paper, the Dense Deployment and Power Assignment Problem (d-DPAP) in Wireless Sensor Networks (WSNs) is defined, and a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) hybridized with a problem-specific Generalized Subproblem-dependent Heuristic (GSH), is proposed. In our method, the d-DPAP is decomposed into a number of scalar subproblems. The subproblems are optimized in parallel, by using neighbourhood information and problem-specific knowledge. The proposed GSH probabilistically alternates between six d-DPAP specific strategies, which are designed based on various WSN concepts and according to the subproblems objective preferences. Simulation results have shown that the proposed hybrid problem-specific MOEA/D performs better than the general-purpose MOEA/D and NSGA-II in several WSN instances, providing a diverse set of high-quality near-optimal network designs to facilitate the decision making process. The behavior of the MOEA/D-GSH in the objective space is also discussed.