An Energy-Efficient Coverage Optimization Method for the Wireless Sensor Networks Based on Multi-objective Quantum-Inspired Cultural Algorithm

The energy-efficiency coverage of wireless sensor network is measure by the network cover rate and the node redundancy rate. To solve this multi-objective optimization problem, a multi-objective quantum-inspired cultural algorithm is proposed, which adopts the dual structure to effectively utilize the implicit knowledge extracted from the non-dominating individuals set to promote more efficient search. It has three highlights. One is the rectangle's height of each allele is calculated by non-dominated sort among individuals. The second is the crowding degree that records the density of non-dominated individuals in the topological cell measure the uniformity of the Pareto-optimal set instead of the crowding distance. The third is the update operation of quantum individuals and the selection operator are directed by the knowledge. Simulation results indicate that the layout of wireless sensor network obtained by this algorithm have larger network cover rate and less node redundancy rate.

[1]  M. Pacheco,et al.  Quantum-Inspired Evolutionary Algorithm for Numerical Optimization , 2006 .

[2]  Ammar W. Mohemmed,et al.  A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram , 2009, 2009 International Conference on Networking, Sensing and Control.

[3]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[4]  Mohamed Batouche,et al.  A Quantum Inspired Evolutionary Framework for Multi-objective Optimization , 2005, EPIA.

[5]  Congfu Xu,et al.  Sensor deployment optimization for detecting maneuvering targets , 2005, 2005 7th International Conference on Information Fusion.

[6]  Sanghamitra Bandyopadhyay,et al.  Multiobjective GAs, quantitative indices, and pattern classification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[8]  Shi Yu,et al.  A real-coded quantum clone multi-objective evolutionary algorithm , 2011, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet).

[9]  Robert G. Reynolds,et al.  Multi-objective Cultural Algorithms , 2010, IEEE Congress on Evolutionary Computation.

[10]  J.-W. Lee,et al.  Energy-Efficient Coverage of Wireless Sensor Networks Using Ant Colony Optimization With Three Types of Pheromones , 2011, IEEE Transactions on Industrial Informatics.

[11]  Shigeru Fujimura,et al.  Multi-Objective Quantum Evolutionary Algorithm for Discrete Multi-Objective Combinational Problem , 2010, 2010 International Conference on Technologies and Applications of Artificial Intelligence.

[12]  Han Shuang,et al.  Optimal Sensor Node Distribution Based on the New Quantum Genetic Algorithm , 2008 .

[13]  Wang Guang-xinga,et al.  Optimal coverage scheme based on genetic algorithm in wireless sensor networks , 2007 .