CAJ | 학술논문

为了克服基本人工鱼群算法( AFSA)收敛速度慢、求解精度不高和易陷入局部最优的不足,提出了一种新颖的人工鱼群算法( AO-AFSA).该算法结合人工鱼与粒子群( PSO)中的粒子都具有个体学习能力和社会学习能力,模拟粒子群中粒子的速度位置更新公式去分别修改人工鱼群算法中人工鱼的觅食行为、聚群行为、追尾行为的更新公式.并采用5个典型的测试函数进行仿真实验,分析算法的寻优精度、收敛速度以及稳定性.测试结果表明改进后的算法能够较快地收敛至全局较优解,有更强的稳定性,并具有较好的寻优性能.
위료극복기본인공어군산법( AFSA)수렴속도만、구해정도불고화역함입국부최우적불족,제출료일충신영적인공어군산법( AO-AFSA).해산법결합인공어여입자군( PSO)중적입자도구유개체학습능력화사회학습능력,모의입자군중입자적속도위치경신공식거분별수개인공어군산법중인공어적멱식행위、취군행위、추미행위적경신공식.병채용5개전형적측시함수진행방진실험,분석산법적심우정도、수렴속도이급은정성.측시결과표명개진후적산법능구교쾌지수렴지전국교우해,유경강적은정성,병구유교호적심우성능.
In order to overcome the slow convergence,low precision and local optimum of basic artificial fish swarm algorithm ( AFSA),this paper proposes a novel artificial fish swarm algorithm.This algorithm takes advantage of individual learning ability and social learning ability of particles in artificial fish swarm and particle swarm,simulates the speed and position of particles in particle swarm to update and modify the formulas of foraging,grouping and tailing.It then experiments five typical testing functions,analyzes the optimization precision,convergence speed and stability of the algorithm.The results turn out that the improved algorithm has stronger convergence,more stability and better performance.