SpartaPlex: A deterministic algorithm with linear scalability for massively parallel global optimization of very large-scale problems. (April 2022)

• Record Type: Journal Article
• Authors: Albert, Benjamin Alexander; Zhang, Arden Qiyu
• Is Part Of: Advances in engineering software. Volume 166(2022)
• Publication Date: 2022-04
• Subjects: Black-box optimization -- Deterministic algorithm -- Global optimization -- Gpu clusters -- Large-scale optimization -- Linear scaling -- High-performance Computing -- Parallel scalability; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103090
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A novel black-box optimizer, SpartaPlex, outperforms 11 state-of-the-art methods. SpartaPlex scales linearly with respect to dimensionality and computing resources. SpartaPlex efficiently uses function evaluations and executes many times faster. Optimizers are evaluated across a wide range of dimensions and benchmark functions. SpartaPlex outperforms on mechanical, structural, and antenna design challenges. Abstract: SpartaPlex is a novel black-box optimization algorithm that yields superior results to state-of-the-art optimizers under tight function evaluation budgets. SpartaPlex is compared with 11 state-of-the-art optimization algorithms on 24 n -dimensional and 6 application-based benchmark problems including mechanical, structural, and antenna design optimization. Using identical computing resources, SpartaPlex finds superior objective function solutions and, as dimensionality increases, executes at least an order of magnitude faster than prior state-of-the-art. SpartaPlex is additionally evaluated for scalability using a compute cluster with 46, 080 cores across 16 Graphical Processing Units. Tested in up to 100, 000 dimensions, SpartaPlex demonstrates linear scalability in time with respect to dimensionality and computational resources.