基于分段搜索策略的改进蜂群算法

针对基本人工蜂群算法在解决优化问题时收敛速度不够快、易陷入局部最优的缺陷,提出一种改进蜂群算法.该算法采用“分段搜索”方式对食物源进行贪婪更新,以提高食物源更新的成功率；同时,招募所有观察蜂选择当前最优食物源,以实现对最优食物源的充分优化.对经典测试函数反复实验的结果表明,改进算法计算结果稳定,与基本蜂群算法相比,加速收敛效果非常明显,全局搜索能力显著提高,运行时间大大缩短.

Modified artificial bee colony algorithm based on segmental-search strategy

For the problem that when using the basic artificial bee colony(ABC) algorithm to solve the optimal problems, it can not converge so fast and can trap in a local optimal solution easily.Therefore,a modified artificial bee colony algorithm is proposed.To improve the updating rate of food sources,the segmental-search strategy is used,and the way of the onlookers choosing the food sources is modified to optimize the best food source fully.Many experiment results of classic functions show that the modified algorithm owns steady performance,has great advantage of convergence property and global optimizing ability,and also runs faster than ABC algorithm.