CAJ | 학술논문

为克服人工蜂群算法在求解函数优化问题时存在收敛精度低、收敛速度慢的缺点，提出一种改进的人工蜂群算法。为提高人工蜂群算法的局部搜索能力和避免早熟收敛，跟随蜂在当前最优解的周围进行局部搜索，并随着迭代次数的增加，逐渐缩小侦查蜂在当前最优解周围的局部搜索范围。通过6个标准测试函数完成仿真实验，结果表明，与基本人工蜂群算法相比，改进算法在寻优精度和收敛速度上均得到提高。
위극복인공봉군산법재구해함수우화문제시존재수렴정도저、수렴속도만적결점，제출일충개진적인공봉군산법。위제고인공봉군산법적국부수색능력화피면조숙수렴，근수봉재당전최우해적주위진행국부수색，병수착질대차수적증가，축점축소정사봉재당전최우해주위적국부수색범위。통과6개표준측시함수완성방진실험，결과표명，여기본인공봉군산법상비，개진산법재심우정도화수렴속도상균득도제고。
An efficient modified Artificial Bee Colony( ABC) algorithm is proposed for function optimization problems to overcome the drawbacks of low computational accuracy and slow convergence of conventional ABC algorithm. In this algorithm,in order to enhance the local search capability of the ABC algorithm, and avoid the premature convergence effectively,onlooker bees do the local search around the current optimal solution,and the radius of the search around the current optimal solution for scout bees is gradually decreased with the increase of iterations. Simulation results of six standard functions show that compared with the basic ABC algorithm,the modified ABC algorithm can attain significant improvement on solution accuracy and convergence rate.