文献类型：期刊文献

英文题名：An improved animal migration optimization algorithm based on interactive learning behavior for high dimensional optimization problem

作者：Lai, Zhaolin[1]; Yu, Huiqun[1]; Feng, Xiang[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2019

起止页码：110

外文期刊名：2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019

收录：EI(收录号：20192807156349)

语种：英文

外文关键词：Benchmarking - Learning algorithms - Learning systems - Animals

摘要：Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, the performance of AMO is degraded rapidly when the dimensionality is larger than 30. In order to overcome this shortcoming, an improved animal migration algorithm (IAMO) based on interactive learning behavior is proposed in this paper. First, we introduce an interactive learning behavior that individuals will learn from each other by exchanging information. During the search process, the search step is dynamically adjusted. In this case, the intelligence of IAMO is higher than AMO. Second, a refined search method is used to search around the current solutions, and this method can enhance the search ability of the algorithm. Third, a birth-and-death mechanism is designed to avoid local optimum. The effectiveness of IAMO is verified on 100 dimensional benchmark functions, and the empirical results show that the performance of IAMO is promising. ? 2019 IEEE.