摘要
针对连续函数优化问题, 给出了一种基于蚂蚁群体智能搜索的随机搜索算法, 对目标函数没有可微的要求, 可有效克服经典算法易于陷入局部最优解的常见弊病。对基本的蚁群算法做了一定的改进, 通过几个函数寻优的结果表明, 算法具有良好的效果。同时, 运用遗传算法对蚁群算法中的一些重要参数进行了寻优, 提高了蚁群算法的收敛速度。
To solve continuous function optimization problems, a new stochastic search algorithm based on ant swarm intelligence is introduced . This algorithm needn't continuous evaluation of derivatives for the object function and it can conquer the shortcomings which classic algorithms are apt to fall into the local optimum. At the same time, in order to reduce the number of function evaluations required for convergence, the basic CACO algorithm is improved. The improved algorithm has been tested for variety of different benchmark test functions, and it can handle these optimization problems very well. Furthermore, genetic algorithm is illustrated to optimize the parameters related to the ant colony algorithm, so that the convergence speed of the ant colony algorithm is improved.
出处
《计算机测量与控制》
CSCD
2005年第3期270-272,共3页
Computer Measurement &Control
