A novel discrete differential evolution algorithm combining transfer function with modulo operation for solving the multiple knapsack problem

被引:2
|
作者
Wang, Lina [1 ]
He, Yichao [1 ,2 ]
Wang, Xizhao [3 ]
Zhou, Zihang [1 ]
Ouyang, Haibin [4 ]
Mirjalili, Seyedali [5 ,6 ]
机构
[1] Hebei GEO Univ, Coll Informat & Engn, Shijiazhuang 050031, Hebei, Peoples R China
[2] Hebei Key Lab Optoelect Informat & Geodetect Techn, Shijiazhuang 050031, Peoples R China
[3] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
[4] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Peoples R China
[5] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimizat, Brisbane, Qld 4006, Australia
[6] Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea
关键词
Differential evolution; Multiple knapsack problem; Repair and optimization; Transfer functions; Modulo operation; OPTIMIZATION; PACKING;
D O I
10.1016/j.ins.2024.121170
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, an efficient method for solving multiple knapsack problem (MKP) using discrete differential evolution is proposed. Firstly, an integer programming model of MKP suitable for discrete evolutionary algorithm is established. Secondly, a new method for discretizing continuous evolutionary algorithm is proposed based on the combination of transfer function and modulo operation. Therefrom, a new discrete differential evolution algorithm (named TMDDE) is proposed. Thirdly, the algorithm GROA is proposed to eliminate infeasible solutions of MKP. On this basis, a new method for solving MKP using TMDDE is proposed. Finally, the performance of TMDDE using S-shaped, U-shaped, V-shaped, and Taper-shaped transfer functions combined with modulo operation is compared, respectively. It is pointed out that T3-TMDDE which used Taper-shaped transfer function T3 is the best. The comparison results of solving 30 MKP instances show that the performance of T3-TMDDE is better than five advanced evolutionary algorithms. It not only indicates that TMDDE is more competitive for solving MKP, but also demonstrates that the proposed discretization method is an effective method.
引用
收藏
页数:25
相关论文
共 50 条
  • [21] A Novel Bat algorithm of solving 0-1 Knapsack Problem
    Chen, Yanfeng
    PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY, 2016, 60 : 1598 - 1601
  • [22] An Efficient Differential Evolution Algorithm for Solving 0-1 Knapsack Problems
    Ali, Ismail M.
    Essam, Daryl
    Kasmarik, Kathryn
    2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2018, : 126 - 133
  • [23] Solving 0-1 knapsack Problems by a Discrete Binary Version of Differential Evolution
    Chen Peng
    Li Jian
    Liu Zhiming
    2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL II, PROCEEDINGS, 2008, : 513 - +
  • [24] A Differential Evolution Algorithm with Variable Neighborhood Search for Multidimensional Knapsack Problem
    Tasgetiren, M. Fatih
    Pan, Quan-Ke
    Kizilay, Damla
    Suer, Gursel
    2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2015, : 2797 - 2804
  • [25] Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem
    Razavi, Seyedeh Fatemeh
    Sajedi, Hedieh
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2015, 29 (05) : 2247 - 2258
  • [26] A Binary Differential Evolution with Adaptive Parameters Applied to the Multiple Knapsack Problem
    Andre, Leanderson
    Parpinelli, Rafael Stubs
    NATURE-INSPIRED COMPUTATION AND MACHINE LEARNING, PT II, 2014, 8857 : 61 - 71
  • [27] A BRANCH AND BOUND ALGORITHM FOR SOLVING THE MULTIPLE-CHOICE KNAPSACK-PROBLEM
    DYER, ME
    KAYAL, N
    WALKER, J
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 1984, 11 (02) : 231 - 249
  • [28] Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem
    Yu Xue
    Yi Zhuang
    Tianquan Ni
    Siru Ni
    Xuezhi Wen
    Journal of Systems Engineering and Electronics, 2014, 25 (01) : 59 - 68
  • [29] Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem
    Xue, Yu
    Zhuang, Yi
    Ni, Tianquan
    Ni, Siru
    Wen, Xuezhi
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2014, 25 (01) : 59 - 68
  • [30] A novel discrete differential evolution algorithm for SVRPSPD
    Hou, Lingjuan
    Hou, Zhijiang
    SENSORS, MEASUREMENT AND INTELLIGENT MATERIALS, PTS 1-4, 2013, 303-306 : 2227 - +