FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm

Jassim Alfadhli; Ali Jaragh; Mohammad Gh Alfailakawi; Imtiaz Ahmad

doi:10.1007/s00521-022-07034-6

FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm

Neural Comput Appl. 2022;34(13):11163-11175. doi: 10.1007/s00521-022-07034-6. Epub 2022 Mar 6.

Authors

Jassim Alfadhli¹, Ali Jaragh¹, Mohammad Gh Alfailakawi¹, Imtiaz Ahmad¹

Affiliation

¹ Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, 13060 Kuwait.

Abstract

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.

Keywords: Fluctuant population (FP); Metaheuristic algorithm (MA); Population adaptation; Population diversity; Slime mould algorithm (SMA).