JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (2): 33-36.doi: 10.6040/j.issn.1672-3961.2.2014.045

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Quantum ant colony algorithm based on the game theory

WANG Qiming1,2, LI Zhanguo1,2, FAN Aiwan1,2   

  1. 1. School of Computer Science and Technology, Pingdingshan University, Pingdingshan 467002, Henan, China;
    2. School of Software Engineering, Pingdingshan University, Pingdingshan 467002, Henan, China
  • Received:2014-04-01 Revised:2015-01-30 Online:2015-04-20 Published:2014-04-01

Abstract: Local optimum and low convergence rate were the main problems when used Quantum ant colony algorithm to solve combinatorial optimization, a quantum ant colony algorithm based on game theory (GQACA) was put forward. The algorithm generated a game sequence by the repeated game model, which made every game produce maximum benefit and get Nash equilibrium of the corresponding game process. Five typical test functions were used to make experiment test on the optimal performance of the GQACA algorithm.The experiments showed that the convergence precision and stability of the GQACA algorithm were superior to QACA algorithm and ACA algorithm.

Key words: the game theory, nash equilibrium, function optimization, combinatorial optimization, quantum ant colony algorithm

CLC Number: 

  • TP393
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