CAJ | 학술논문

人工蜂群算法作为一种新生代的优化算法，近年来在众多科学领域中表现出一定的优势，但是其收敛速度并不高效，并且容易过早地陷入局部最优。首先通过对适应度选择进行改进，提高算法的收敛速度，同时结合模拟退火算法，一定程度上避免过早陷入局部最优。最后用一组基准函数进行实验，证明改进后的人工蜂群算法有更好的优化性能。
인공봉군산법작위일충신생대적우화산법，근년래재음다과학영역중표현출일정적우세，단시기수렴속도병불고효，병차용역과조지함입국부최우。수선통과대괄응도선택진행개진，제고산법적수렴속도，동시결합모의퇴화산법，일정정도상피면과조함입국부최우。최후용일조기준함수진행실험，증명개진후적인공봉군산법유경호적우화성능。
As a new optimization algorithm, artificial bee colony algorithm shows a certain advantages in many scientific fields over the years. But its convergence speed is not efficient, and it can get into local optimization easily. Improves the convergence speed through the improve-ment of fitness selection. The algorithm is combined with simulated annealing algorithm at the same time , which avoids falling into local optimum easily in some extent. The experiments which use a set of benchmark functions show that the improved artificial bee colony al-gorithm can get better optimization performance.