一种求解旅行商问题的迭代改进蚁群优化算法

传统的蚁群优化算法每次都从头开始构造新解,无条件地接收选择的解部件,该策略削弱了算法的局部求精能力。针对该不足,提出了一种求解旅行商问题的迭代改进蚁群优化算法。在构造解的过程中,蚂蚁始终记忆一个完整的解,并且只接受能够改进解的候选城市。使用解的部分重构策略来保持种群的多样性,以避免早熟收敛。仿真结果表明迭代改进蚁群优化算法能在更少的迭代次数内获得更好的解。

A kind of iterative improvement based ant colony optimization algorithm for the traveling salesman problem

Classical ant colony optimization algorithms build solutions by starting with an empty initial solution,and unconditionally accepting selected components.This has become a natural restriction of its intensification ability.To overcome this shortage,an iterative improvement based ant colony optimization algorithm was presented for the traveling salesman problem.In the process of constructing the solution,the ant always memorizes a complete solution;and it adopts a candidate city only when such an adoption can improve the solution.Reconstructing of a partial solution was used to keep the diversity of swarm and avoid premature convergence.Simulation results showed that the proposed algorithm can obtain better solutions within less iteration numbers.