Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Information Sciences: an International Journal
On the deployment of wireless data back-haul networks
IEEE Transactions on Wireless Communications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
IEEE Communications Magazine
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The aim of this paper is to find a deployed sensor node arrangement to maximize the area of coverage, minimize the net energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes maintaining connectivity between each sensor node and the sink node for proper data transmission. We have also assumed tree structure of communication between the deployed nodes and the sink node for data transmission. We have modeled the sensor node deployment problem as a multi-objective constrained problem maintaining all the above requirements. We have proposed a new fuzzy dominance based decomposition technique called MOEA/DFD and have compared its performance on other contemporary state-of-arts in multi-objective optimization field like MOEA/D and NSGAII. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. MOEA/DFD performs better than all other algorithms.