Quantum-behaved Particle Swarm Optimization with Cooperative Coevolution for Large Scale Optimization

摘要：

　　Quantum-behaved particle swarm optimization(QPSO)has successfully been applied to unimodal and multimodal optimization problems.However,with the emerging and popular of big data and deep machine learning,QPSO encounters limitations with high dimensions.In this paper,QPSO with cooperative coevolution(QPSO_CC)is used to decompose the high dimensional problems into several lower dimensional problems and optimize them separately.The numerical experimental results show that QPSO_CC has comparative or even better performance than other algorithms.

关键词： large scale quantum-behaved particle swarm optimization cooperative coevolution domain decompositimponent

作者: Na Tian

作者单位: Department of Educational Technology,Jiangnan University,Wuxi 214122,China

会议类型: 国际会议

会议名称: The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

会议地点: 贵阳

会议语种:英文

页码: 82-85

在线出版日期: 2015-08-18（万方平台首次上网日期，不代表论文的发表时间）