A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

被引:9
|
作者
Wang, Zhaocai [1 ]
Bao, Xiaoguang [1 ]
Wu, Tunhua [2 ]
机构
[1] Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China
[2] Wenzhou Business Coll, Sch Informat Engn, Wenzhou 325035, Peoples R China
基金
中国国家自然科学基金;
关键词
POLYNOMIAL-TIME; OPTIMIZATION ALGORITHM; DNA ALGORITHM; SOLVE; COEXISTENCE; SYSTEM;
D O I
10.1155/2021/8814947
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Multiple-colony ant algorithm for parallel assembly line balancing problem
    Ozbakir, Lale
    Baykasoglu, Adil
    Gorkemli, Beyza
    Gorkemli, Latife
    APPLIED SOFT COMPUTING, 2011, 11 (03) : 3186 - 3198
  • [22] Parallel Computing-Based Dynamics Model for Tracking Moving Targets
    Cui, Yugang
    JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING, 2022, 16 (01) : 79 - 86
  • [23] Parallel design of intelligent optimization algorithm based on FPGA
    Zou, Xiaofu
    Wang, Lina
    Tang, Yue
    Liu, Yilong
    Zhan, Shicheng
    Tao, Fei
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 94 (9-12) : 3399 - 3412
  • [24] A swap sequence based Artificial Bee Colony algorithm for Traveling Salesman Problem
    Khan, Indadul
    Maiti, Manas Kumar
    SWARM AND EVOLUTIONARY COMPUTATION, 2019, 44 : 428 - 438
  • [25] NUMERICAL OPTIMIZATION ALGORITHM BASED ON GENETIC ALGORITHM FOR A DATA COMPLETION PROBLEM
    Jouilik, B.
    Daoudi, J.
    Tajani, C.
    Abouchabaka, J.
    TWMS JOURNAL OF APPLIED AND ENGINEERING MATHEMATICS, 2023, 13 (01): : 86 - 97
  • [26] A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem
    Gulcu, Saban
    Mahi, Mostafa
    Baykan, Omer Kaan
    Kodaz, Halife
    SOFT COMPUTING, 2018, 22 (05) : 1669 - 1685
  • [27] Intelligent optimization algorithm grid computing-based applications
    Liu, Bingjie
    Zhu, Li
    Ren, Jianlan
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (04) : 5201 - 5211
  • [28] A new approach for solving the flow‐shop scheduling problem using a parallel optimization algorithm
    Habibeh Nazif
    Journal of Ambient Intelligence and Humanized Computing, 2021, 12 : 10723 - 10732
  • [29] A hybrid genetic algorithm, list-based simulated annealing algorithm, and different heuristic algorithms for travelling salesman problem
    Ilin, Vladimir
    Simic, Dragan
    Simic, Svetislav D.
    Simic, Svetlana
    Saulic, Nenad
    Luis Calvo-Rolle, Jose
    LOGIC JOURNAL OF THE IGPL, 2023, 31 (04) : 602 - 617
  • [30] A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model
    Cao, Rong-Fei
    Wang, Xing-Ce
    Wu, Zhong-Ke
    Zhou, Ming-Quan
    Liu, Xin-Yu
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2016, 31 (02) : 400 - 416