CAJ | 학술논문

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）。
침대양자의군산법구해조합우화문제시역함입국부최우화수렴속도만적문제，제출일충기우박혁론적양자의군산법（quantum ant colony algorithm based on the game theory，GQACA）。산법채용중복박혁모형，재중복박혁중산생일개박혁서렬，사득매차박혁도능구산생최대효익，병득도상응박혁과정적납십균형。이용전형적5개표준측시함수대 GQACA 산법심우성능진행시험측시。시험결과표명：GQACA 산법적수렴정도화은정성균요우우양자의군산법（quantum ant colony algorithm，QACA）화의군산법（ant colony algorithm，ACA）。
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 for-ward.The algorithm generated a game sequence by the repeated game model,which made every game produce maxi-mum 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 pre-cision and stability of the GQACA algorithm were superior to QACA algorithm and ACA algorithm.