CAJ | 학술논문

　　粒子群优化算法是由Kennedy和Eberhart[1,2]在1995年提出的一种基于群体智能的随机进化算法，是在鸟群、鱼群和人类社会行为规律的启发下提出来的。针对粒子群优化算法容易陷入局部极小的缺陷，对粒子群优化算法的速度进化公式进行改进，将粒粒子行为基于个体极值和全局极值变化为基于个体极值的加权平均、全局极值和按概率选择其它粒子的个体极值。新算法更符合生物的学习规律，使得粒子充分利用整个种群的信息，保证了群体的多样性。
　　입자군우화산법시유Kennedy화Eberhart[1,2]재1995년제출적일충기우군체지능적수궤진화산법，시재조군、어군화인류사회행위규률적계발하제출래적。침대입자군우화산법용역함입국부겁소적결함，대입자군우화산법적속도진화공식진행개진，장립입자행위기우개체겁치화전국겁치변화위기우개체겁치적가권평균、전국겁치화안개솔선택기타입자적개체겁치。신산법경부합생물적학습규률，사득입자충분이용정개충군적신식，보증료군체적다양성。
Particle swarm optimization algorithm is proposed by Kennedy and Eberhart [1,2] in 1995, which is a stochastic evolutionary algorithm based on swarm intelligence and is inspired by birds, fish and human social behavior. Aiming at the defect that particle swarm optimization algorithm is easy to fall into local minimum, the speed of evolution equation of particle swarm optimization algorithm is improved, the particle behavior of individual extremum and global extreme changing into individual extremum based on weighted average, the global extremum and other particles chosen by probability. The new algorithm is more consistent with the laws of biological learning, making full use of the information of the whole population of particles and ensuring the diversity of the group.