CAJ | 학술논문

针对人工蜂群算法存在早熟现象和搜索过程容易陷入局部最优的缺陷，提出一种引入佳点集和猴群翻过程的改进人工蜂群算法。首先利用佳点集方法构造均匀的初始种群，通过保持种群的多样性，在一定程度上避免早熟现象，加快算法的收敛速度。然后学习猴群算法的翻过程，在寻优过程中使种群的搜索区域进行转移，从而跳出局部最优，得到全局最优解。在经典测试函数和CEC05测试函数集上的实验结果表明，与标准人工蜂群算法以及其它改进算法相比，算法性能优越，具有很强的鲁棒性和快速收敛的能力，且有效避免陷入局部最优。
침대인공봉군산법존재조숙현상화수색과정용역함입국부최우적결함，제출일충인입가점집화후군번과정적개진인공봉군산법。수선이용가점집방법구조균균적초시충군，통과보지충군적다양성，재일정정도상피면조숙현상，가쾌산법적수렴속도。연후학습후군산법적번과정，재심우과정중사충군적수색구역진행전이，종이도출국부최우，득도전국최우해。재경전측시함수화CEC05측시함수집상적실험결과표명，여표준인공봉군산법이급기타개진산법상비，산법성능우월，구유흔강적로봉성화쾌속수렴적능력，차유효피면함입국부최우。
An improved Artificial Bee Colony ( ABC) algorithm with good point set and turn process of Monkey algorithm ( MA) is proposed to overcome the defections of the basic ABC algorithm. Firstly, aiming at the defects of premature convergence, the good point set is used to initialize the population, which can generate a homogeneous population to keep the diversity of a swarm. Besides, the turn process of MA is introduced to help the swarm to jump out of the local optima and to get the global optimal solution. Simulation results on standard test functions and benchmark functions of CEC05 show that the proposed algorithm outperforms the basic algorithm and other improved ABC algorithms on both the precision and the convergence rate.