Abstract:As the population size of the micro-population teaching and learning optimization algorithm is small, it is hard to maintain its population diversity. To improve the search performance of the micro-population teaching-learning-based optimization algorithm, a micro-population teaching-learning-based optimization algorithm based on multi-source gene learning (MTLBO-MGL) is proposed. In MTLBO-MGL, the teaching stage and the learning stage are used to evolve individuals at the gene level via the random selection strategy. Moreover, the population diversity is detected at the gene level and the sparse spectral clustering is utilized to cluster the population on each dimension. Different evolutionary strategies are selected to improve the search performance of the proposed algorithm based on the diversity detection result and the clustering result. The performance of the proposed algorithm is compared with the other four micro-population evolutionary algorithms on 28 test functions. The simulation results prove that the overall performance of the proposed algorithm is significantly better than the other four compared algorithms. The proposed algorithm is also applied to solve the UAV 3D path planning problem, and the results show that MTLBO-MGL can achieve better results on this scenario.