基于人工蜂群算法的TSP仿真

针对标准蚁群算法求解的旅行商问题(TSP)存在收敛速度慢,易陷入局部最优等缺陷,将求解组合优化问题的过程转化为蜜蜂群寻找优良蜜源的过程,并分析了人工蜂群算法及3种基本算法模型,3种引领因子更新策略,讨论了转移因子动态更新公式及状态转移公式,研究了利用该算法求解TSP问题的具体步骤,通过典型的TSP实例进行了仿真实验,结果表明该算法能够克服早熟现象,迭代次数少,收敛速度快,通用性强,比标准蚁群算法具有一定优势.

Simulation on Traveling Salesman Problem(TSP) Based on Artificial Bees Colony Algorithm

Aimed at the defects such as slow convergence and easy to fall into local optimization for standard ant colony algorithm to solve traveling salesman problem(TSP). Combinatorial optimization problem is transformed to searching farina for honey bees, based on the analysis of nectar searching, and artificial bee colony algorithm with three basic models is analyzed. Three regenerative strategies of leading gene are discussed. Dynamic renew formula of transforming gene and transferring formula of state are established and honeybee algorithm model. At last, concrete process of solving TSP by adopting ABC is proposed and some typical TSP samples are practiced. The results show that this algorithm can avoid pre-maturity and advance constringency and the algorithm has more advantages than ant standard colony algorithm.