An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application

被引:126
作者
Cai, Xing [1 ]
Zhao, Huimin [1 ]
Shang, Shifan [1 ]
Zhou, Yongquan [3 ]
Deng, Wu [1 ]
Chen, Huayue [2 ]
Deng, Wuquan [4 ]
机构
[1] Civil Aviat Univ China, Coll Elect Informat & Automat, Tianjin 300300, Peoples R China
[2] China West Normal Univ, Sch Comp Sci, Nanchong 637002, Peoples R China
[3] Guangxi Univ Nationalities, Coll Artificial Intelligence, Nanning 530006, Peoples R China
[4] Chongqing Univ, Cent Hosp, Dept Endocrinol, Key Lab Biorheol Sci & Technol,Minist Educ, Chongqing 400014, Peoples R China
基金
中国国家自然科学基金;
关键词
Quantum-inspired evolutionary algorithm; Cooperative co-evolutionary algorithm; Multi-strategy; Knapsack problem; Airport gate allocation; AIRPORT GATE ASSIGNMENT; OPTIMIZATION;
D O I
10.1016/j.eswa.2021.114629
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In order to overcome the slow convergence speed, poor global search ability and difficult designing rotation angle of quantum-inspired evolutionary algorithm (QEA), an improved quantum-inspired cooperative co evolution algorithm based on combining the strategies of cooperative co-evolution, random rotation direction and Hamming adaptive rotation angle, namely MSQCCEA is proposed, which is employed to propose a new airport gate allocation optimization method in this paper. In the proposed MSQCCEA, the cooperative co evolution strategy is used to improve the global search capability. The random rotation direction strategy is developed to change the quantum evolution direction from one to two in order to avoid local optimal solution and realize the full search of the solution space. A new Hamming adaptive rotation angle strategy is designed to enable individuals to adaptively adjust the rotation angle according to the difference degree between the individual and the target individual, so as to improve the global search ability and convergence speed. A new airport gate allocation optimization method using MSQCCEA is realized to effectively allocate airport gates to the flights. Finally, the knapsack problem and the actual airport gate allocation problem are used to verify the effectiveness of the proposed MSQCCEA and gate allocation optimization method, respectively. The comparison experiment results demonstrate that the proposed MSQCCEA has faster convergence speed and higher convergence accuracy, and the proposed gate allocation optimization method takes on great potential to make decisions for actual airport management.
引用
收藏
页数:13
相关论文
共 54 条
  • [1] Al-Sultan A, 2010, P JKSC 2010 JOIN M J, P159
  • [2] [Anonymous], 1999, Evolutionary computation: Theory and applications
  • [3] A Bi-Objective Constrained Robust Gate Assignment Problem: Formulation, Instances and Algorithm
    Cai, Xinye
    Sun, Wenxue
    Misir, Mustafa
    Tan, Kay Chen
    Li, Xiaoping
    Xu, Tao
    Fan, Zhun
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2021, 51 (09) : 4488 - 4500
  • [4] Optimizing the codon usage of synthetic gene with QPSO algorithm
    Cai, Yujie
    Sun, Jun
    Wang, Jie
    Ding, Yanrui
    Tian, Na
    Liao, Xiangru
    Xu, Wenbo
    [J]. JOURNAL OF THEORETICAL BIOLOGY, 2008, 254 (01) : 123 - 127
  • [5] Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies
    Chen, Hao
    Heidari, Ali Asghar
    Chen, Huiling
    Wang, Mingjing
    Pan, Zhifang
    Gandomi, Amir H.
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 111 (111): : 175 - 198
  • [6] An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models
    Chen, Huiling
    Jiao, Shan
    Heidari, Ali Asghar
    Wang, Mingjing
    Chen, Xu
    Zhao, Xuehua
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2019, 195 : 927 - 942
  • [7] Fusion of Multi-RSMOTE With Fuzzy Integral to Classify Bug Reports With an Imbalanced Distribution
    Chen, Rong
    Guo, Shi-Kai
    Wang, Xi-Zhao
    Zhang, Tian-Lun
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2019, 27 (12) : 2406 - 2420
  • [8] A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem
    Cui, Ligang
    Wang, Lin
    Deng, Jie
    Zhang, Jinlong
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2013, 2013
  • [9] A New Teaching-Learning-based Chicken Swarm Optimization Algorithm
    Deb, Sanchari
    Gao, Xiao-Zhi
    Tammi, Kari
    Kalita, Karuna
    Mahanta, Pinakeswar
    [J]. SOFT COMPUTING, 2020, 24 (07) : 5313 - 5331
  • [10] An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network
    Deng, Wu
    Liu, Hailong
    Xu, Junjie
    Zhao, Huimin
    Song, Yingjie
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (10) : 7319 - 7327