所属单位：杭州电子科技大学
教研室：信息与自动化研究所
发表刊物：IEEE Trans. Systems, Man and Cybernetics: Systems
项目来源：中国国家自然科学基金，项目号：61572224、61304082；国家杰出青年科学基金，项目号：61425009。
关键字：Differential mutation, experience information,global optimization.
摘要：Teaching–learning-based optimization (TLBO) is an intelligent optimization algorithm with relatively fewer parameters that should be determined in updating equations. For solving complex optimization problems, the local optima often appear in the evolution. To decrease the possibility of this phenomenon, a novel TLBO variant (EI-TLBO) with experience information (EI) and differential mutation is presented. In the method, neighborhood information (the best individual NTeacher and the mean individual NMean) of each learner's neighbors is introduced to improve the exploration capability.
备注：SCI, WOS: 000382175700005; EI, Accession Number: 20163502754512. This paper was recommended by Associate Editor Z. Li.
合写作者：Feng Zou, Debao Chen, Renquan Lu*,王卓
论文类型：基础研究
文献类型：期刊
是否译文：否
发表时间：2016-09-16