A consensus optimization mechanism with Q-learning-based distributed PSO for large-scale group decision-making DOI
Qingyang Jia, Kewei Yang, Yajie Dou

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 93, P. 101841 - 101841

Published: Jan. 8, 2025

Language: Английский

An Improved Artificial Bee Colony Algorithm With Q-Learning for Solving Permutation Flow-Shop Scheduling Problems DOI
Hanxiao Li, Kaizhou Gao, Peiyong Duan

et al.

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2022, Volume and Issue: 53(5), P. 2684 - 2693

Published: Nov. 16, 2022

A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with $Q$ -learning, named QABC, solving it minimizing the maximum completion (makespan). First, Nawaz–Enscore–Ham (NEH) heuristic is employed initialize population of ABC. Second, set problem-specific and knowledge-based neighborhood structures are designed employ phase. -learning favorably choose premium structures. Next, all-round search strategy proposed further enhance quality individuals onlooker Moreover, insert-based method applied avoid local optima. Finally, QABC used solve 151 well-known benchmark instances. Its performance verified by comparing state-of-the-art algorithms. Experimental statistical results demonstrate superiority over peers concerned problems.

Language: Английский

Citations

116
Co-Evolution With Deep Reinforcement Learning for Energy-Aware Distributed Heterogeneous Flexible Job Shop Scheduling DOI
Rui Li, Wenyin Gong, Ling Wang

et al.

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2023, Volume and Issue: 54(1), P. 201 - 211

Published: Sept. 6, 2023

Energy-aware distributed heterogeneous flexible job shop scheduling (DHFJS) problem is an extension of the traditional FJS, which harder to solve. This work aims minimize total energy consumption (TEC) and makespan for DHFJS. A deep $Q$ -networks-based co-evolution algorithm (DQCE) proposed solve this NP-hard problem, includes four parts: First, a new co-evolutionary framework proposed, allocates sufficient computation global searching executes local search surrounding elite solutions. Next, nine features-based operators are designed accelerate convergence. Moreover, -networks applied learn select best operator each solution. Furthermore, efficient heuristic method reduce TEC. Finally, 20 instances real-world case employed evaluate effectiveness DQCE. Experimental results indicate that DQCE outperforms six state-of-the-art algorithms

Language: Английский

Citations

48
A Q-learning-based hyper-heuristic evolutionary algorithm for the distributed flexible job-shop scheduling problem with crane transportation DOI

Zi-Qi Zhang,

Fang-Chun Wu,

Bin Qian

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 234, P. 121050 - 121050

Published: Aug. 1, 2023

Language: Английский

Citations

46
A hybrid genetic tabu search algorithm for distributed flexible job shop scheduling problems DOI
Jin Xie, Xinyu Li, Liang Gao

et al.

Journal of Manufacturing Systems, Journal Year: 2023, Volume and Issue: 71, P. 82 - 94

Published: Sept. 9, 2023

Language: Английский

Citations

45
A Learning-Driven Multi-Objective cooperative artificial bee colony algorithm for distributed flexible job shop scheduling problems with preventive maintenance and transportation operations DOI

Zhengpei Zhang,

Yaping Fu, Kaizhou Gao

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 196, P. 110484 - 110484

Published: Aug. 18, 2024

Language: Английский

Citations

26
MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects DOI
Zeyu Zhang, Zhongshi Shao, Weishi Shao

et al.

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

Published: Jan. 10, 2024

Language: Английский

Citations

20
Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection DOI
Fei Ming, Wenyin Gong, Ling Wang

et al.

IEEE/CAA Journal of Automatica Sinica, Journal Year: 2024, Volume and Issue: 11(4), P. 919 - 931

Published: March 28, 2024

Language: Английский

Citations

20
A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities DOI
Cong Luo, Wenyin Gong, Fei Ming

et al.

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

Published: Feb. 2, 2024

Language: Английский

Citations

19
HGNP: A PCA-based heterogeneous graph neural network for a family distributed flexible job shop DOI

Jiake Li,

Junqing Li, Ying Xu

et al.

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110855 - 110855

Published: Jan. 1, 2025

Language: Английский

Citations

5
Scheduling of stochastic distributed hybrid flow-shop by hybrid estimation of distribution algorithm and proximal policy optimization DOI
Lin Luo, Xuesong Yan

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126523 - 126523

Published: Jan. 1, 2025

Language: Английский

Citations

5