CAJ | 학술논문

针对标准粒子群算法在处理复杂优化问题时易出现收敛速度慢和陷入局部最优的问题，提出了一种自适应进化模型的粒子群优化算法。通过设定的阈值 limit将种群进化状态划分为正常状态和“早熟”状态，当种群全局最优位置信息连续超过 limit次没有更新时，认为算法处于“早熟”状态，此时对种群的个体最优位置进行反向学习，帮助算法逃离局部最优，并采用新的进化模型；否则视为正常进化状态，并采用标准粒子群进化模型。8个基准测试函数的仿真结果表明，该算法与一些其它改进粒子群算法如FIPS、CLPSO、MPSO-SFLA算法相比，在全局寻优能力、收敛速度和收敛精度方面都具有明显的优势。
침대표준입자군산법재처리복잡우화문제시역출현수렴속도만화함입국부최우적문제，제출료일충자괄응진화모형적입자군우화산법。통과설정적역치 limit장충군진화상태화분위정상상태화“조숙”상태，당충군전국최우위치신식련속초과 limit차몰유경신시，인위산법처우“조숙”상태，차시대충군적개체최우위치진행반향학습，방조산법도리국부최우，병채용신적진화모형；부칙시위정상진화상태，병채용표준입자군진화모형。8개기준측시함수적방진결과표명，해산법여일사기타개진입자군산법여FIPS、CLPSO、MPSO-SFLA산법상비，재전국심우능력、수렴속도화수렴정도방면도구유명현적우세。
As standard particle swarm optimization algorithm had some shortcomings ,such as converging slowly and getting trapped in the local minima ,a new improved PSO algorithm based on adaptive evolution was proposed .The algorithm’s popula-tion evolution state was divided into “normal and premature”by setting the threshold value limit .When population’s global opti-mal position was not updated continuously for more than limit times ,the algorithm was considered to be in the “premature”state .At this point ,the individuals’ optimal position adopted opposition-based learning strategy which helped the algorithm to escape from local optimum ,and the algorithm adopted the improved evolution model .Otherwise ,it was considered to be in“nor-mal”evolution state ,and the standard model was adopted .The simulation experiment results on eight classical benchmark func-tions showed that the capacity of searching optimal solution ,convergence speed and convergence accuracy of the AEMPSO was better than some of the common improved particle swarm optimizations ,such as FIPS ,CLPSO and MPSO-SFLA .