基于自适应选择策略的人工蜂群算法

Self-adaptive selection strategy for artificial bee colony algorithm

由于适应度比例选择法在进化过程中使得蜜源的多样性受限和早熟收敛.因此,按照蜜源当前的性状提出了一种基于自适应选择策略的蜂群算法(SABC)来动态地调节选择压力,使算法的全局搜索和局部搜索能力达到平衡.从测试函数的仿真结果表明:改进的人工蜂群算法很大地提高了蜂群算法的寻优能力,在收敛速度和精度上优于基本蜂群算法.

The fitness-proportionate selection is the basic selection method for artificial bee colony algorithm, but it results in the limited diversity and the premature convergence of nectar. Therefore, the paper proposes a self- adaptive selection strategy for artificial bee colony （SABC） to adjust dynamically the selection intensity according to the change of the population state, making the balance of the global search and local search. The experimental results show that the algorithm has improved the global optimizing ability and has great convergence property.