Alfadhli Jassim, Jaragh Ali, Alfailakawi Mohammad Gh, Ahmad Imtiaz
Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, 13060 Kuwait.
Neural Comput Appl. 2022;34(13):11163-11175. doi: 10.1007/s00521-022-07034-6. Epub 2022 Mar 6.
In this paper, an adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA) is proposed. Unlike the original SMA where population size is fixed in every epoch, FP-SMA will adaptively change population size in order to effectively balance exploitation and exploration characteristics of SMA's different phases. Experimental results on 13 standard and 30 IEEE CEC2014 benchmark functions have shown that FP-SMA can achieve significant reduction in run time while maintaining good solution quality when compared to the original SMA. Typical saving in terms of function evaluations for all benchmarks was between 20 and 30% on average with a maximum being as high as 60% in some cases. Therefore, with its higher computation efficiency, FP-SMA is much more favorable choice as compared to SMA in time stringent applications.
本文提出了一种自适应波动种群规模黏菌算法（FP-SMA）。与原始黏菌算法在每个时期种群规模固定不同，FP-SMA将自适应地改变种群规模，以便有效平衡黏菌算法不同阶段的利用和探索特性。在13个标准测试函数和30个IEEE CEC2014基准函数上的实验结果表明，与原始黏菌算法相比，FP-SMA在保持良好解质量的同时，能显著减少运行时间。所有基准测试函数评估次数的典型节省平均在20%到30%之间，在某些情况下最高可达60%。因此，由于其更高的计算效率，在时间紧迫的应用中，与黏菌算法相比，FP-SMA是更优的选择。
