CAJ | 학술논문

提出了一种AEA (Alopex-based evolutionary algorithm)的改进算法NAEA。该算法从AEA迭代过程的每一代种群中选择若干适应度值较高的个体，并假设被选中的个体服从正态分布。根据该分布重新采样产生新个体，选择最好的若干新个体替换原种群同等数量的最差个体。在计算正态分布的均值时，采用个体适应度值的大小来分配权值，从而使得算法可以更快更准地收敛到函数最优值。22个标准测试函数的优化结果表明：改进后的算法与 AEA相比，不仅在寻优能力方面有了很大的提高，并且收敛速度也有了显著的提升。最后，将NAEA应用在重油热解模型的参数估计中，良好的结果也反映了NAEA算法的良好性能和实际应用价值。
제출료일충AEA (Alopex-based evolutionary algorithm)적개진산법NAEA。해산법종AEA질대과정적매일대충군중선택약간괄응도치교고적개체，병가설피선중적개체복종정태분포。근거해분포중신채양산생신개체，선택최호적약간신개체체환원충군동등수량적최차개체。재계산정태분포적균치시，채용개체괄응도치적대소래분배권치，종이사득산법가이경쾌경준지수렴도함수최우치。22개표준측시함수적우화결과표명：개진후적산법여 AEA상비，불부재심우능력방면유료흔대적제고，병차수렴속도야유료현저적제승。최후，장NAEA응용재중유열해모형적삼수고계중，량호적결과야반영료NAEA산법적량호성능화실제응용개치。
An improved AEA—NAEA is proposed. In the NAEA, several individuals with high fitness value chosen from the population were assumed to follow a normal distribution. The worst individuals in original population were replaced with the good individuals sampled from the normal distribution. In this paper, individual fitness value was used to calculate the mean of the normal distribution, so as to make the algorithm faster and more accurately converge to the optimal value. The optimization results tested by 22 benchmark functions show that compared with AEA the NAEA has achieved significant improvement in both optimizing capability and convergence rate. Finally, the NAEA was applied to estimate the parameters of heavy oil thermal cracking three lumps model, which also demonstrates that the NAEA is an algorithm with good performance and practical application value.