报 告 人：李维德 教授
WOA is a newly developed meta-heuristic algorithm which performs well in solving optimization problems. We added two strategies to the original WOA algorithm. The first is to use chaos mechanism to generate initial value to improve the convergence speed of the algorithm. The second is to use the opposition-based learning method to balance the exploration and development ability of the algorithm to help the algorithm jump out of local optimal solutions. The proposed algorithm is compared with other algorithms on unimodal functions, multimodal functions and fixed dimensional multimodal functions, and it is applied to a famous engineering design problem. The results show that the combination of the two strategies can improve the convergence speed and enhance the global search ability of the original WOA algorithm. The OBCWOA algorithm proposed in this paper is a potential algorithm with better performance than other existing algorithms.
李维德，兰州大学数学与统计学院教授、硕士生导师。长期从事应用数学和应用统计的教学与研究工作，主要研究领域为生态模型、数据分析和机器学习。在SCI源刊杂志、中文核心期刊等刊物上发表文章60多篇。是Knowledge-Based Systems, Expert Systems with Applications, Ecological Modelling, Neurocomputing, Physics A 等几十个国际刊物的审稿人。