基于非合作博弈模型的量子蚁群算法

Quantum Ant Colony Algorithm Based on Non-cooperative Game Theory

针对量子蚁群算法求解组合优化问题时易陷入局部最优和收敛速度慢的问题,提出一种基于非合作博弈模型的量子蚁群算法(quantum ant colony algorithm based on non-cooperative game theory,NGQACA),采用重复博弈模型,在重复博弈中产生一个博弈序列,使得每次博弈都能够产生最大效益,并得到了相应博弈过程的纳什均衡。利用三个典型的标准测试函数对此算法进行实验测试,实验结果表明本文基于非合作博弈模型的量子蚁群算法的收敛精度和稳定性均要优于量子蚁群算法(quantum ant colony algorithm,QACA)和蚁群算法(ant colony algorithm,ACA)。

Quantum ant colony algorithm is easy to fall into the situation of local optimum and slow convergence rate when solving combinatorial optimization problem. This paper puts forward a quantum ant colony algorithm based on non-cooperative game theory（NGQACA）. Adopted in this algorithm, the repeated game model can produce a game sequence to make every game produce maximum benefit, and then it can get the corresponding game process of Nash equilibrium. The there typical test functions are used for testing the performance of NGQACA algorithm optimization. The experimental results show that the convergence precision and stability of NGQACA are better than QACA and ACA algorithm.