Improving the performance and energy of non-dominated sorting for evolutionary multiobjective optimization on GPU/CPU platforms

DOI10.1007/S10898-018-0669-3zbMath1402.90172OpenAlexW2806261984MaRDI QIDQ721173

D. Kharzeev

Publication date: 18 July 2018

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-018-0669-3

zbMATH Keywords

high performance computingGPUnon-dominated sortingmulticore platformsbest order sortevolutionary multiobjective algorithms

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (2)

A parallel naive approach for non-dominated sorting: a theoretical study considering PRAM CREW model ⋮ A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems

Uses Software

• CUDA
• SPEA2
• Omni-optimizer

Cites Work

• Nonlinear multiobjective optimization
• Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU
• Very Fast Non-dominated Sorting
• Evolutionary Multi-Criterion Optimization
• Evolutionary Multi-Criterion Optimization

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