计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (12): 2797-2808.DOI: 10.3778/j.issn.1673-9418.2104006

• 人工智能 • 上一篇    下一篇

引入特征迁移和匹配学习的双蚁型蚁群算法

陈达1, 游晓明1,+(), 刘升2   

  1. 1.上海工程技术大学 电子电气工程学院,上海 201620
    2.上海工程技术大学 管理学院,上海 201620
  • 收稿日期:2021-04-01 修回日期:2021-06-07 出版日期:2022-12-01 发布日期:2021-06-15
  • 通讯作者: +E-mail: yxm6301@163.com
  • 作者简介:陈达(1997—),男,江苏盐城人,硕士研究生,主要研究方向为智能算法、移动机器人路径规划、嵌入式系统。
    游晓明(1963—),女,江苏兴化人,博士,教授,硕士生导师,主要研究方向为群智能系统、分布式并行处理、进化算法。
    刘升(1966—),男,湖北大冶人,博士,教授,硕士生导师,主要研究方向为量子启发式进化算法、分布式并行处理、进化算法。
  • 基金资助:
    国家自然科学基金(61673258);国家自然科学基金(61075115)

Dual Ant Colony Algorithm Based on Backtracking Migration and Matching Learning

CHEN Da1, YOU Xiaoming1,+(), LIU Sheng2   

  1. 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620,;China
    2. School of Management, Shanghai University of Engineering Science, Shanghai 201620, China
  • Received:2021-04-01 Revised:2021-06-07 Online:2022-12-01 Published:2021-06-15
  • About author:CHEN Da, born in 1997, M.S. candidate. His research interests include intelligent algorithm, path planning of mobile robot and embedded system.
    YOU Xiaoming, born in 1963, Ph.D., professor, M.S. supervisor. Her research interests include swarm intelligent systems, distributed parallel processing and evolutionary computing.
    LIU Sheng, born in 1966, Ph.D., professor, M.S. supervisor. His research interests include quantum inspired evolutionary computation, distributed parallel processing and evolutionary computing.
  • Supported by:
    National Natural Science Foundation of China(61673258);National Natural Science Foundation of China(61075115)

摘要:

针对传统蚁群算法在求解旅行商问题(TSP)时存在收敛速度慢、易陷入局部最优等问题,提出一种引入特征迁移学习和匹配学习的双蚁型蚁群算法(BMACS)。首先,将种群动态分级为探索蚁和追踪蚁,其中适应度较高的为探索蚁,较低的为追踪蚁;其次,提出一种局部特征迁移机制,该机制下有两种策略,在特征迁移策略中,将探索蚁公共路径作为局部特征通过局部信息素奖励迁移到信息素矩阵中,进而提高探索蚁的影响力,加快算法收敛速度;在变异学习策略中,追踪蚁跟随探索蚁负责对次优路径的探索,自适应重构探索蚁路径,从而丰富种群多样性;最后,当算法停滞时,利用匹配学习机制将当前最优个体与相似度最高的历史最优个体进行交流学习,重组信息素,增加种群的多样性,进而提高算法跳出局部最优的能力。使用MATLAB对TSPLIB中的多组案例进行仿真实验,结果表明改进后的算法平衡了多样性和收敛速度,有效提高了解的质量。

关键词: 蚁群算法, 旅行商问题(TSP), 局部特征迁移, 匹配学习, 动态分级

Abstract:

In order to solve the problems of slow convergence speed and easy to fall into local optimum of tradi-tional ant colony algorithm in solving traveling salesman problem (TSP), the dual ant colony algorithm based on backtracking migration and matching learning (BMACS) is proposed. Firstly, the population is dynamically graded into exploration ants and tracking ants. The ants with higher fitness are exploration ants and the ones with lower fitness are tracking ants. Secondly, a local feature transfer mechanism is proposed, in which there are two strategies: in the feature transfer strategy, the pheromone on the common path of exploration ants is rewarded, and this path is transferred into the pheromone matrix as a local feature, so as to improve the influence of exploration ants and acce-lerate the convergence speed of the algorithm; in the mutation learning strategy, the tracking ants are responsible for exploring the suboptimal path, and adaptively reconstruct the path of exploration ants to enrich the diversity of the population. Finally, when the algorithm stagnates, the matching learning mechanism is used to communicate and learn between the current optimal individual and the historical optimal individual with the highest similarity, and the pheromone is reorganized to increase the diversity of the population, thus improving the ability of the algorithm to jump out of the local optimal. Simulation experiments are carried out with MATLAB for multiple sets of cases in TSPLIB, and the results show that the improved algorithm balances diversity and convergence speed, and effecti-vely improves the quality of solution.

Key words: ant colony algorithm, traveling salesman problem (TSP), local feature migration, matching learning, dyna-mic classification

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