Aiming at the problem of slow convergence and being easy to fall into local optimum in the route planning of the traditional teaching-learning-based optimization algorithm, an adaptive crossover teaching-learning-based optimization algorithm was proposed. Firstly, the teaching factor of the algorithm was changed with the number of iterations, so the learning speed of the algorithm was improved. Secondly, when the algorithm was likely to fall into local optimum, a certain disturbance was added to make the algorithm jump out of local optimum as far as possible. Finally, in order to improve the convergence effect, the crossover link of genetic algorithm was introduced into the algorithm. Then the path planning of Unmanned Aerial Vehicle (UAV) was carried out by using the traditional teaching-learning-based optimization algorithm, the adaptive crossover teaching-learning-based optimization algorithm and the Quantum Particle Swarms Optimization (QPSO) algorithm. The simulation results show that in 10 times of planning, the adaptive crossover teaching-learning-based optimization algorithm finds the global optimal route for 8 times, while the traditional teaching-learning-based optimization algorithm and the QPSO algorithm find the route for only 2 times and 1 time respectively, and the convergence of the adaptive crossover teaching-learning-based optimization algorithm is faster than the other two algorithms.

To deal with the “hot area” problem and cluster heads selection in clustering routing algorithm of Wireless Sensor Network (WSN), the paper designed an uneven clustering routing algorithm based on adaptive Particle Swarm Optimization (PSO). Firstly, according to the distance between candidate nodes and sink node, the competitive radius was calculated and clusters of various sizes were constructed. Then this paper introduced the PSO according to the cluster size. The PSO was used to select the final cluster heads by evaluating factors such as residual energy of nodes and distance between nodes. The cluster heads with more residual energy were chosen as the next hop to form multi-top route in which the sink node is the root. The simulation results show that compared with other two similar algorithms, LEACH and EUCC, the proposed algorithm extends 34% and 16% of survival time of network separately, reduces 22% and 12% of average energy consumption respectively, and effectively decreases the network nodes energy consumption.