Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System

被引:15
作者
Zhang, Jiarui [1 ]
Wang, Gang [2 ]
Song, Yafei [2 ]
机构
[1] Air Force Engn Univ, Grad Coll, Xian 710051, Shaanxi, Peoples R China
[2] Air Force Engn Univ, Air & Missile Def Coll, Xian 710051, Shaanxi, Peoples R China
关键词
multi-agent system; contract net protocol; task assignment; response threshold; pheromone; COMMUNICATION; COLONY;
D O I
10.3390/a12040070
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model and the flow of pheromone model were established, then the complete task allocation process was designed. Three experimental settings were simulated under different conditions. Results: When the number of agents was 20 and the maximum load value was L(max=)3, the traffic and run-time of task allocation under the improved contract net protocol decreased. When the number of tasks and L-max was fixed, the improved contract net protocol had advantages over the dynamic contract net and classical contract net protocols in terms of both traffic and run-time. Setting up the number of agents, tasks and L-max to improve the task allocation under the contract net not only minimizes the number of errors, but also the task completion rate reaches 100%. Conclusions: The improved contract net protocol can reduce the traffic and run-time compared with classical contract net and dynamic contract net protocols. Furthermore, the algorithm can achieve better assignment results and can re-forward all erroneous tasks.
引用
收藏
页数:13
相关论文
共 27 条
  • [1] [Anonymous], 2007, J SOFTW TOOLS TECHNO
  • [2] Buehler J, 2014, AAAI CONF ARTIF INTE, P2527
  • [3] An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination
    Cao, Yongcan
    Yu, Wenwu
    Ren, Wei
    Chen, Guanrong
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, 9 (01) : 427 - 438
  • [4] Carbo J, 2016, ADCAIJ-ADV DISTRIB C, V5, P1
  • [5] [陈坚 CHEN Jian], 2010, [计算机仿真, Computer Simulation], V27, P113
  • [6] Distributed Control for Groups of Unmanned Aerial Vehicles Performing Surveillance Missions and Providing Relay Communication Network Services
    de Moraes, R. S.
    de Freitas, E. P.
    [J]. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2018, 92 (3-4) : 645 - 656
  • [7] Ant system: Optimization by a colony of cooperating agents
    Dorigo, M
    Maniezzo, V
    Colorni, A
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (01): : 29 - 41
  • [8] Giuseppe C, 2013, J INTELL MANUF, V24, P405
  • [9] A multi-agent system for the classification of gender and age from images
    Gonzalez-Briones, Alfonso
    Villarrubia, Gabriel
    De Paz, Juan F.
    Corchado, Juan M.
    [J]. COMPUTER VISION AND IMAGE UNDERSTANDING, 2018, 172 : 98 - 106
  • [10] Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review
    Gonzalez-Briones, Alfonso
    De la Prieta, Fernando
    Mohamad, Mohd Saberi
    Omatu, Sigeru
    Corchado, Juan M.
    [J]. ENERGIES, 2018, 11 (08):