CAJ | 학술논문

为了克服基本人工鱼群算法收敛速度慢、求解精度不高和易陷入局部最优的不足,提出了自适应调整人工鱼群算法参数的方法,该方法采用个体鱼适应值与整个鱼群的平均适应值作比较,将整个鱼群分为三组,再采用自适应调整每组鱼群的视野范围和步长的方法,对基本鱼群算法进行了优化和改进.应用四个典型的测试函数进行仿真实验,分析算法的寻优精度、收敛速度及稳定性.实验结果表明改进后的算法能够较快地收敛至全局较优解,并具有较好的寻优性能.
위료극복기본인공어군산법수렴속도만、구해정도불고화역함입국부최우적불족,제출료자괄응조정인공어군산법삼수적방법,해방법채용개체어괄응치여정개어군적평균괄응치작비교,장정개어군분위삼조,재채용자괄응조정매조어군적시야범위화보장적방법,대기본어군산법진행료우화화개진.응용사개전형적측시함수진행방진실험,분석산법적심우정도、수렴속도급은정성.실험결과표명개진후적산법능구교쾌지수렴지전국교우해,병구유교호적심우성능.
In order to overcome the limitations lying in slow convergence, poor accuracy and the propensity of getting into a lo-cal best answer in Artificial Fish-Swarm Algorithm(AFSA), improved measures, which adaptively tuning algorithm parameter of artificial fish-swarm, are presented. This measure accords the comparison between the adaptive value of individual fish and the average adaptive value of the fish-swarm, dividing the whole fish into three groups which including better fish, general fish and poor fish, adaptively turning the step length and view field of fish in each group, in order to optimize and improve the basic algorithm. It analyzes the adapting fish-swarm step length and view field, optimization accuracy, convergence speed and stability of algorithm through four typical test function simulation. It can get the conclusion that adapting fish-swarm’s field of view and steps can make the algorithm converge quickly to the global better solution, and has good optimization performance.