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Improved Wolf Pack Algorithm Based on Tent Chaotic Mapping and Levy Flight

Wolf pack algorithm is one of the group intelligence algorithms, which has advantages in convergence rate and objective function solving precision. But there still exists deficiency: slow convergence speed, easy to fall into the local extremum, the searching precision is not ideal and so on. In this paper, The Tent chaotic mapping strategy is used to make the population distribution even more uniform. Levy flight characteristic is used to improve the searching strategy of wolves, which makes the algorithm can skip the local optimum in the later convergence process and improve the searching precision of the wolf pack algorithm. By comparing similar algorithms (Wolf colony algorithm based on the leader strategy (LWCA)and Wolf pack algorithm based on improved search strategy (MWPA)), the experiment results of 6 complex standard functions show that the TLWPA algorithm has faster convergence speed and higher accuracy.