Particle swarm optimization research base on quantum Q-learning behavior

被引：2
|
作者：
Li L. [1 ]
Wu S. [2 ]
机构：
[1] Information Center, Changsha Health Vocational College, Changsha
[2] School of Information Science and Engineering, Hunan International Economics University, Changsha
来源：
Journal of Information Technology Research | 2017年 / 10卷 / 01期
关键词：
Particle swarm optimization (PSO); Q-learning; Quantum behavior; Searching model; Selecting parameter;
D O I：
10.4018/JITR.2017010103
中图分类号：
学科分类号：
摘要：
Quantum-behaved Particle Swarm Optimization algorithm is analyzed, contraction-expansion coefficient and its control method are studied. To the different performance characteristics with different coefficients control strategies, a control method of coefficient with Q-learning is proposed. The proposed method can tune the coefficient adaptively, and the whole optimization performance is increased. The comparison and analysis of results with the proposed method, constant coefficient control method, linear decreased coefficient control method and non-linear decreased coefficient control method is given based on CEC 2005 benchmark function. Copyright © 2017, IGI Global.
引用
收藏
页码：29 / 38
页数：9
相关论文
共 50 条
  • [41] Physical synthesis of quantum circuits using Q-learning
    Bu, Dengli
    Bin, Zhiyan
    Sun, Jing
    QUANTUM INFORMATION PROCESSING, 2025, 24 (02)
  • [42] Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity
    Das, P. K.
    Behera, H. S.
    Panigrahi, B. K.
    ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2016, 19 (01): : 651 - 669
  • [43] Swarm Q-Learning With Knowledge Sharing Within Environments for Formation Control
    Tung Nguyen
    Hung Nguyen
    Debie, Essam
    Kasmarik, Kathryn
    Garratt, Matthew
    Abbass, Hussein
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [44] Multi-Swarm Cuckoo Search Algorithm with Q-Learning Model
    Li, Juan
    Xiao, Dan-dan
    Zhang, Ting
    Liu, Chun
    Li, Yuan-xiang
    Wang, Gai-ge
    COMPUTER JOURNAL, 2021, 64 (01): : 108 - 131
  • [45] Oil Production Optimization Using Q-Learning Approach
    Zahedi-Seresht, Mazyar
    Sadeghi Bigham, Bahram
    Khosravi, Shahrzad
    Nikpour, Hoda
    PROCESSES, 2024, 12 (01)
  • [46] Research on Particle Swarm Optimization in Location Assignment Optimization
    Chen, Yueting
    He, Fang
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 111 - 116
  • [47] Hyperparameter optimization of neural networks based on Q-learning
    Xin Qi
    Bing Xu
    Signal, Image and Video Processing, 2023, 17 : 1669 - 1676
  • [48] Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning
    Dong, Xingping
    Shen, Jianbing
    Wang, Wenguan
    Liu, Yu
    Shao, Ling
    Porikli, Fatih
    2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 518 - 527
  • [49] Multi-Agent Q-Learning for Drone Base Stations
    Janji, Salim
    Kliks, Adrian
    2023 19TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS, WIMOB, 2023, : 261 - 266
  • [50] Optimization of industrial robot grasping processes with Q-learning
    Belke, Manuel
    Joeressen, Till
    Petrovic, Oliver
    Brecher, Christian
    2023 5TH INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS, ICCR, 2023, : 113 - 119
12345