Scalable parallel evolutionary optimization on high performance computing

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 lar...

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Vydané v:Aerospace Traffic and Safety Ročník 1; číslo 2-4; s. 93 - 102
Hlavní autori: Jin, Chen, Zheng, Daren, He, Shuke, Cheng, Ao, Liu, Gang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2024
Predmet:
Asynchronous migration
Computational efficiency
High performance computing (HPC)
Parallel evolutionary algorithm
High performance computing (HPC)
Computational efficiency
Parallel evolutionary algorithm
Asynchronous migration
ISSN:2950-3388, 2950-3388
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Popis
Shrnutí: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.
ISSN:2950-3388
2950-3388
DOI:10.1016/j.aets.2024.12.006