文献类型：期刊文献

英文题名：imBBO: An Improved Biogeography-Based Optimization Algorithm

作者：Shi, Kai[1,2]; Yu, Huiqun[1]; Fan, Guisheng[1]; Yang, Xingguang[1]; Song, Zheng[3]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, 201112, China; [3] The Third Research Institute of the Ministry of Public Security, Shanghai, China

年份：2019

卷号：11204 LNCS

起止页码：284

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20191506764451)

语种：英文

外文关键词：Genetic algorithms - Heuristic algorithms - Ecology - Benchmarking

摘要：Biogeography based Optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. However, its direct-copying-based migration and random mutation operators make it easily possess local exploitation ability. To enhance the performance of BBO, we propose an improved BBO algorithm called imBBO. A hybrid migration operation is designed to further improve the population diversity and enhance the algorithm exploration ability. Empirical results demonstrate that our imBBO effectively gains the high optimization performance by comparing with the original BBO and three BBO variants for 23 out of 30 CEC’2017 benchmarks. Moreover, our imBBO presents a faster convergence speed. ? 2019, Springer Nature Switzerland AG.