混合蜂群杂草算法及其在灌溉制度优化中的应用

A hybrid algorithm of bee colony and invasive weed optimization and its application in irrigation schedule optimization

首先针对杂草算法容易早熟收敛的问题,将人工蜂群算法的寻优机制引入其中,提出了一种混合蜂群杂草算法。该算法对杂草种群中的每个个体利用采蜜蜂搜索方式进行变异,对群体最优个体利用跟随蜂搜索方式进行变异,用较优的变异结果替代原有个体,提高了算法的收敛精度。然后,通过对几个标准测试函数进行实验,验证了改进算法的优化性能。最后,将该算法应用到灌溉制度优化问题中,为制定灌溉水量分配方案提供了一种新的工具。

Firstly, aiming at the premature convergence problem of Invasive Weed Optimization （IWO） algorithm,a hybrid algorithm of Bee Colony and Invasive Weed Optimization （BCIWO） is pro- posed by introducing the optimization mechanism of Artificial Bee Colony （ABC） algorithm. Every indi- vidual in the weed colony is mutated by employed bees＇ search behavior and the global best individual is mutated by onlookers＇ search method in this improved algorithm. The better result of mutation is used to replace the original individual. Those improve the convergence speed and the accuracy of IWO. Then the optimal performance of BCIWO is verified by test functions. Finally, the new algorithm is applied into the irrigation schedule optimization problem, thus providing a promising way to allocate irrigation water.