Abstract
Compared with the advantages and disadvantages of genetic algorithm, based on the ant colony algorithm, this paper combined with the selection, crossover and mutation operation of genetic algorithm, the search speed and optimization ability of ant colony algorithm are improved. The optimal path evaluation function considers nodes. The energy consumption and the residual energy of the node enable the nodes with more residual energy to participate in the data forwarding preferentially and balance the energy consumption between the nodes. The comparison with the classical ant colony algorithm and the genetic algorithm shows that as the number of data forwarding rounds increases, the improved The ant colony algorithm has low energy consumption, many residual energy, and the network life cycle is obviously prolonged. With the increase of the network running time, the improved ant colony algorithm, the node equalization energy consumption is good, and the success rate of the optimal path search is also significantly better than the other two algorithms.
Published Version
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