Handling many-objective optimisation problems with R2 indicator and decomposition-based particle swarm optimiser

DOI10.1080/00207721.2018.1552765zbMath1484.90102OpenAlexW2903249631WikidataQ128822490 ScholiaQ128822490MaRDI QIDQ5027940

Pei-Qiu Huang, Fei Li, Jian chang Liu, Xiang-yong Kong

Publication date: 7 February 2022

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2018.1552765

zbMATH Keywords

particle swarm optimisation (PSO)R2 indicatorbi-level archiveelitist learning strategy (ELS)many-objective optimisation problems (MaOPs)objective space decomposition

Mathematics Subject Classification ID

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

Related Items (3)

A distribution-knowledge-guided assessment strategy for multiobjective particle swarm optimization ⋮ An efficient multi-objective optimization algorithm based on level swarm optimizer ⋮ A new multi-objective particle swarm optimisation algorithm based on R2 indicator selection mechanism

Uses Software

• MOEA/D
• RM-MEDA
• SMS-EMOA
• HypE
• SMPSO

Cites Work

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• SMS-EMOA: multiobjective selection based on dominated hypervolume
• A diversity enhanced multiobjective particle swarm optimization
• An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms
• Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems

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