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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A new Reinforcement Learning-based Memetic Particle Swarm Optimizer
Hussein Samma, Chee Peng Lim, Junita Mohamad–Saleh
Applied Soft Computing (2016) Vol. 43, pp. 276-297
Closed Access | Times Cited: 130

Showing 1-25 of 130 citing articles:

Ensemble strategies for population-based optimization algorithms – A survey
Guohua Wu, Rammohan Mallipeddi, Ponnuthurai Nagaratnam Suganthan
Swarm and Evolutionary Computation (2018) Vol. 44, pp. 695-711
Closed Access | Times Cited: 230
Policy Iteration Reinforcement Learning-based control using a Grey Wolf Optimizer algorithm
Iuliu Alexandru Zamfirache, Radu‐Emil Precup, Raul‐Cristian Roman, et al.
Information Sciences (2021) Vol. 585, pp. 162-175
Closed Access | Times Cited: 159
Evolutionary Machine Learning: A Survey
Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 8, pp. 1-35
Open Access | Times Cited: 140
A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization
Feng Wang, Xujie Wang, Shilei Sun
Information Sciences (2022) Vol. 602, pp. 298-312
Closed Access | Times Cited: 139
Review of artificial intelligence applications in engineering design perspective
Nurullah Yüksel, Hüseyin Rıza Börklü, Hüseyin Kürşad Sezer, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 118, pp. 105697-105697
Closed Access | Times Cited: 120
Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models
Zhenzhen Hu, Wenyin Gong, Shuijia Li
Energy Reports (2021) Vol. 7, pp. 916-928
Open Access | Times Cited: 105
Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy
Wei Li, Peng Liang, Bo Sun, et al.
Swarm and Evolutionary Computation (2023) Vol. 78, pp. 101274-101274
Closed Access | Times Cited: 48
Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines
Khalid Elbaz, Annan Zhou, Shui‐Long Shen
Tunnelling and Underground Space Technology (2023) Vol. 136, pp. 105104-105104
Closed Access | Times Cited: 40
A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization
Xujie Wang, Feng Wang, Qi He, et al.
Swarm and Evolutionary Computation (2024) Vol. 86, pp. 101486-101486
Closed Access | Times Cited: 19
A reinforcement learning-based ranking teaching-learning-based optimization algorithm for parameters estimation of photovoltaic models
Haoyu Wang, Xiaobing Yu, Yangchen Lu
Swarm and Evolutionary Computation (2025) Vol. 93, pp. 101844-101844
Closed Access | Times Cited: 2
Particle swarm optimizer with crossover operation
Yonggang Chen, Lixiang Li, Jinghua Xiao, et al.
Engineering Applications of Artificial Intelligence (2018) Vol. 70, pp. 159-169
Closed Access | Times Cited: 123
A reinforcement learning approach for dynamic multi-objective optimization
Fei Zou, Gary G. Yen, Lixin Tang, et al.
Information Sciences (2020) Vol. 546, pp. 815-834
Closed Access | Times Cited: 102
Automatic design of hyper-heuristic based on reinforcement learning
Shin Siang Choong, Li‐Pei Wong, Chee Peng Lim
Information Sciences (2018) Vol. 436-437, pp. 89-107
Closed Access | Times Cited: 99
A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems
Maria Amélia Lopes Silva, Sérgio Ricardo de Souza, Marcone Jamilson Freitas Souza, et al.
Expert Systems with Applications (2019) Vol. 131, pp. 148-171
Closed Access | Times Cited: 99
Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems
Amir Seyyedabbasi, Royal Aliyev, Farzad Kiani, et al.
Knowledge-Based Systems (2021) Vol. 223, pp. 107044-107044
Closed Access | Times Cited: 96
Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems
Zhihui Li, Li Shi, Caitong Yue, et al.
Swarm and Evolutionary Computation (2019) Vol. 49, pp. 234-244
Closed Access | Times Cited: 95
Integrated scheduling optimization of U-shaped automated container terminal under loading and unloading mode
Bowei Xu, Depei Jie, Junjun Li, et al.
Computers & Industrial Engineering (2021) Vol. 162, pp. 107695-107695
Closed Access | Times Cited: 66
A reinforcement learning-based hybrid Aquila Optimizer and improved Arithmetic Optimization Algorithm for global optimization
Haiyang Liu, Xingong Zhang, Hanxiao Zhang, et al.
Expert Systems with Applications (2023) Vol. 224, pp. 119898-119898
Closed Access | Times Cited: 34
Reinforcement-learning-based parameter adaptation method for particle swarm optimization
Shiyuan Yin, Jin Min, Huaxiang Lu, et al.
Complex & Intelligent Systems (2023) Vol. 9, Iss. 5, pp. 5585-5609
Open Access | Times Cited: 22
Dynamic multi-swarm differential learning particle swarm optimizer
Yonggang Chen, Lixiang Li, Haipeng Peng, et al.
Swarm and Evolutionary Computation (2017) Vol. 39, pp. 209-221
Closed Access | Times Cited: 86
Differential evolution with neighborhood-based adaptive evolution mechanism for numerical optimization
Mengnan Tian, Xingbao Gao
Information Sciences (2018) Vol. 478, pp. 422-448
Closed Access | Times Cited: 82
A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem
Kamal Z. Zamli, Fakhrud Din, Bestoun S. Ahmed, et al.
PLoS ONE (2018) Vol. 13, Iss. 5, pp. e0195675-e0195675
Open Access | Times Cited: 72
Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks
Shuo Xie, Vittorio Garofano, Xiumin Chu, et al.
Ocean Engineering (2019) Vol. 193, pp. 106609-106609
Open Access | Times Cited: 67
Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey
Imran Rahman, Junita Mohamad–Saleh
Applied Soft Computing (2018) Vol. 69, pp. 72-130
Closed Access | Times Cited: 62
A reinforcement learning-based communication topology in particle swarm optimization
Yue Xu, Dechang Pi
Neural Computing and Applications (2019) Vol. 32, Iss. 14, pp. 10007-10032
Closed Access | Times Cited: 56
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