A reinforcement learning approach for dynamic multi-objective optimization

DOI10.1016/j.ins.2020.08.101zbMath1475.90099OpenAlexW3084357507MaRDI QIDQ2055564

Fei Zou, Lixin Tang, Gary G. Yen, Chun-Feng Wang

Publication date: 1 December 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.08.101

zbMATH Keywords

predictionreinforcement learningdynamic multi-objective optimizationseverity degree

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Learning and adaptive systems in artificial intelligence (68T05)

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Uses Software

• MOEA/D

Cites Work

• Unnamed Item
• Evolutionary multi-objective optimization in uncertain environments. Issues and algorithms
• Convergence results for single-step on-policy reinforcement-learning algorithms
• A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization
• Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm
• Practical issues in temporal difference learning
• \({\mathcal Q}\)-learning
• Global memory schemes for dynamic optimization
• A modified version of a T-Cell Algorithm for constrained optimization problems
• Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

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