一种求解连续对象优化问题的改进蚁群算法

An Improved Ant Colony Optimization Solving Continuous Optimization Problems

蚁群算法在搜索过程中容易陷入局部最优解,且不适用于连续对象优化问题。文章针对这些问题,采用信息量变异、引入微粒群操作等方法进行改进,提出了一种引入微粒群操作的改进蚁群算法,并应用于求解连续对象优化问题。对几个典型复杂连续函数优化问题的测试研究表明,该改进算法不仅跳出局部最优解的能力更强,而且能较快地收敛到全局最优解,表明了算法的有效性。

Ant Colony Optimization （ACO） has the disadvantages such as easily relapsing into local optima and. Aimed at improving this problem existed in ACO, several new betterments are proposed and evaluated. In particular, pheromone mutation and Particle Swarm Optimization operator were inducted. Then an improved Ant Colony Optimization with Particle Swarm Optimization operator was put forward. It was tested by a set of benchmark continuous function optimization problems. And the results of the examples show that it can not easily run into the local optimum and can converge at the global optimum.