Scalable parallel evolutionary optimization on high performance computing  

作　　者：Chen Jin Daren Zheng Shuke He Ao Cheng Gang Liu 

机构地区：[1]Key Laboratory of Civil Aviation Emergency Science&Technology,CAAC,Nanjing University of Aeronautics and Astronautics,Nanjing,China [2]Zhejiang Scientific Research Institute of Transport,Hangzhou,China [3]School of Computer Science and Information Technology,RMIT University,Melbourne,Australia [4]Key Laboratory of Aircraft Environment Control and Life Support,MIIT,Nanjing University of Aeronautics and Astronautics,Nanjing,China [5]Zhejiang Jiande Institute of General Aviation,Hangzhou,China [6]Zhejiang ATM Sub-Bureau.CAAC,Hangzhou,China

出　　处：《Aerospace Traffic and Safety》2024年第2期93-102,共10页空天交通与安全(英文)

基　　金：This research was funded by the Zhejiang’JIANBING’R&D Project(No.2022C01055);Zhejiang Provincial Department of Transport Technology Project(No.2024011).

摘　　要：To improve the efficiency of evolutionary algorithms(EAs)for solving complex problems with large populations,this paper proposes a scalable parallel evolution optimization(SPEO)framework with an elastic asynchronous migration(EAM)mechanism.SPEO addresses two main challenges that arise in large-scale parallel EAs:(1)heavy communication workload from extensive information exchange across numerous processors,which reduces computational efficiency,and(2)loss of population diversity due to similar solutions generated and shared by many processors.The EAM mechanism introduces a self-adaptive communication scheme to mitigate communication overhead,while a diversity-preserving buffer helps maintain diversity by filtering similar solutions.Experimental results on eight CEC2014 benchmark functions using up to 512 CPU cores on the Australian National Computational Infrastructure(NCI)platform demonstrate that SPEO not only scales efficiently with an increasing number of processors but also achieves improved solution quality compared to state-of-the-art island-based EAs.

关 键 词：Parallel evolutionary algorithm High performance computing(HPC) Computational efficiency Asynchronous migration 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]