Guided hyperplane evolutionary algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Optimizing coverage in a K-covered and connected sensor network using genetic algorithms
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
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Wireless Sensor Networks are widely used to monitor the areas. In many WSN applications it is important to achieve a good coverage of the observed area while the energetic efficiency and the lifetime of the network are high. The task of placing the sensor nodes while addressing these objectives is known as WSN layout problem Also, the characteristics and the total number of the sensors influence the final solution of the problem. A new evolutionary technique for multiobjective optimization called Guided Hyper-plane Evolutionary Algorithm (GHEA) has been recently proposed. The originality of the approach consists in the fact that the fitness assignment is realized by using a target hyperplane of the population. GHEA has been tested on consecrated benchmarks and proved to offer better results than consecrated techniques. As the optimal sensors network layout can be reformulated as a multiobjective optimization problem, the paper presents a possible use of GHEA technique for solving the considered problem.