Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 86, P. 101517 - 101517

Published: Feb. 27, 2024

Language: Английский

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A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 85, P. 101467 - 101467

Published: Jan. 1, 2024

Language: Английский

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Hybrid quantum particle swarm optimization and variable neighborhood search for flexible job-shop scheduling problem

Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 73, P. 334 - 348

Published: Feb. 26, 2024

Language: Английский

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MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 85, P. 101479 - 101479

Published: Jan. 10, 2024

Language: Английский

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A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 85, P. 101497 - 101497

Published: Feb. 2, 2024

Language: Английский

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Energy-efficient human-robot collaborative U-shaped disassembly line balancing problem considering turn on-off strategy: Uncertain modeling and solution method

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 38 - 69

Published: Feb. 27, 2025

Language: Английский

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Scheduling Multiobjective Dynamic Surgery Problems via Q-Learning-Based Meta-Heuristics

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2024, Volume and Issue: 54(6), P. 3321 - 3333

Published: Feb. 21, 2024

This work addresses multiobjective dynamic surgery scheduling problems with considering uncertain setup time and processing time. When dealing them, researchers have to consider rescheduling due the arrivals of urgent patients. The goals are minimize fuzzy total medical cost, maximum completion time, maximize average patient satisfaction. First, we develop a mathematical model for describing addressed problems. is expressed by triangular numbers. Then, four meta-heuristics improved, eight variants developed, including artificial bee colony, genetic algorithm, teaching-learning-base optimization, imperialist competitive algorithm. For improving initial solutions' quality, two initialization strategies developed. Six local search proposed fine exploitation $Q$ -learning algorithm used choose suitable among them in iterative process meta-heuristics. states actions defined according characteristic Finally, algorithms tested 57 instances different scales. analysis discussions verify that improved colony most one all compared algorithms.

Language: Английский

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Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109780 - 109780

Published: Oct. 18, 2024

Language: Английский

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A grey wolf optimization algorithm for solving partial destructive disassembly line balancing problem consider feasibility evaluation and noise pollution

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 60, P. 102418 - 102418

Published: Feb. 24, 2024

Language: Английский

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A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 91, P. 101760 - 101760

Published: Nov. 4, 2024

Language: Английский

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