【摘要】 针对人工蜂群算法在更新策略中精度与稳定性不高的问题,提出一种改进的人工蜂群算法。该改进的人工蜂群算法通过增加每次更新维度的个数来改善算法的精度,在文中所选择的每次更新维度的个数为可行解维数的1/2;同时,该算法选择当前适应值最优的蜂蜜源在其周围进行邻域搜索,避免了由于随机性而带来的算法精度降低问题。最后,比较改进的人工蜂群算法与经典的粒子群算法,通过多个高维测试函数的仿真实验表明,改进的人工蜂群算法比粒子群算法具有更高的精度和稳定性,展现了更好的性能。更多还原

【Abstract】 In allusion to the problem that the artificial bee colony algorithm has low accuracy and stability in the update strategy,an improved artificial bee colony algorithm is proposed. The accuracy of the algorithm is improved by increasing the number of dimensions per update,and the selected number of dimensions per update is half of the feasible solution dimensions in this paper;the honeycomb source with the best adaptive value is chosen to conduct a neighbourhood search around it,which avoids the reducing the accuracy of the algorithm caused by the randomness. The simulation results with multi high-dimensional testing functions show that,in comparison with the classical particle swarm optimization algorithm,the improved artificial bee colony algorithm has a higher accuracy and stability,and show better performance.更多还原