Intelligence optimization algorithms have an important application in engineering calculation. Swarm intelligence optimization algorithms belong to the class of intelligent algorithms, which are derived from the simulation of natural biological evolution or foraging behaviours. But these algorithms have sometimes slow convergence, and for multi-peak problems, they are easy to fall into the local optimal solution in the later period of the algorithms. To solve these problems, in this paper, the

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... dvantages of these algorithms and the wolf colony search algorithm based on the strategy of the leader (LWCA) algorithm are used, to propose an improved wolf colony search algorithm with the strategy of the leader algorithm (ILWCA). ILWCA is based on mutual communication by the sensor perception of wireless networking, adding a global update strategy and a step acceleration network. In addition, by introducing the concept of individual density to depict the distribution density of the wolf, the problem of excessive input parameters of traditional wolf group algorithm is solved. Moreover, by adding the way of mutual migration, the algorithm increases the communication between wolves, and strengthens the overall performance of the optimization process. Finally, the experimental results show that the ILWCA algorithm achieves higher solution accuracy and better robustness compared with the accuracy and robustness of particle swarm optimization (PSO), gravitational search algorithm (GSA), swarm optimization algorithm (SOA), grey wolf optimizer (GWO), and genetic algorithms (GA).