Computer Science ›› 2014, Vol. 41 ›› Issue (9): 132-136.doi: 10.11896/j.issn.1002-137X.2014.09.025

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Energy Consumption in Ad hoc Based on Quantum-behaved Particle Swarm Optimization Elitist Learning Algorithm

ZHANG Feng,JIA Zhi-ping,CAI Xiao-jun and ZHANG Lan-hua   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In Ad hoc networks,with the emerging of multicast applications,how to construct a multicast tree of the minimum energy consumption is an important problem.For the effect of the different choices of relay nodes on the construction of the minimum energy consumption multicast tree,quantum-behaved particle and elitist learning swarm optimization algorithm (QPELSO) to optimize the construction of the minimum energy consumption multicast tree was proposed.In order to avoid the premature convergence of the particle swarm optimization algorithm.This method exerts the dynamic-approximation search strategy on the elitist particles to avoid them running into local optima and provides a good guidance for the population.While the algorithm is found to be in a dead state according to the premature judgment mechanism,the mutative-scale chaotic perturbation is used to exhibit a wide range exploration and keep the balance of exploration and exploitation.The results of simulated experiments show that the modified discrete particle swarm optimization algorithm has strong optimization ability,and can effectively optimize the construction of the minimum energy consumption multicast tree.

Key words: Ad hoc networks,Elitist learning,Quantum-behaved particle swarm optimization,Multicast routing,Minimum energy consumption

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