蚁群算法参数优化

针对蚁群算法运行参数选取问题，提出一种利用粒子群优化算法对蚁群算法的运行参数进行优化选择的方法。将蚁群算法的运行参数作为粒子群的位置信息，在算法迭代过程中使用粒子的当前位置作为算法参数，运行蚁群算法求解标准优化问题，设计适应值评价函数对求解性能做出评价，引导粒子向着适应值高的方向趋近。仿真结果表明，该算法能够方便有效地实现对蚁群算法运行参数的优化选取。

Ant Colony Algorithm Parameters Optimization

For the problem of ant colony algorithm parameters selection, a method of optimum parameter selection using particle swam optimization algorithm is proposed. In this method, the parameters are set as the position information of particle swam in the algorithm iteration process. Then ant colony algorithm is eXecuted to solve the standard optimization problem, and a fitness evaluation function is designed to evaluate the performance of the solution. The particles are navigated to the direction of a higher fitness. Simulation results show that this algorithm selects the optimal operation parameters effectively.