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- 2015
基于博弈论的量子蚁群算法
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Abstract:
摘要： 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,提出一种基于博弈论的量子蚁群算法(quantum ant colony algorithm based on the game theory, GQACA)。算法采用重复博弈模型,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,并得到相应博弈过程的纳什均衡。利用典型的5个标准测试函数对GQACA算法寻优性能进行试验测试。试验结果表明: GQACA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)。
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
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