多源基因学习的微种群教与学优化及应用

由于微种群教与学优化算法的种群规模较小, 故其种群多样性很难维持. 为提高微种群教与学优化算法的搜索性能, 提出了一种基于多源基因学习的微种群教与学优化算法(micro-population teaching-learning-based optimization based on multi-source gene learning, MTLBO-MGL). 在MTLBO-MGL算法中, 将教阶段和学阶段根据随机选择策略来对个体进行基因水平上的进化操作; 并从基因层面上对种群多样性进行检测和使用稀疏谱聚类方法对种群的每个维度进行聚类. 然后, 根据多样性检测和聚类结果, 选择不同的进化策略来提高所提算法的搜索性能. 在28个测试函数上, 通过将所提算法与其他4种微种群进化算法作对比, 证明了所提算法的整体性能要显著好于所对比的4种算法. 本文还将所提算法应用于无人机三维路径规划问题, 结果表明MTLBO-MGL算法能够在该问题上取得较好结果.

Micro-population Teaching-learning-based Optimization Based on Multi-source Gene Learning and Its Application

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.