Improved Shuffled Flog Leaping Algorithm Based on Keeping the Diversity of Population
ZHANG Qiang
LIU Lijie
GUO Hao
Abstract：Shuffled flog leaping algorithm for optimization in function easily falls into local optimal solution and the pre‐mature quickly converges of such shortcomings .An improved shuffled flog leaping algorithm is proposed based on cloud model theory .The idea is to initialize the population through reverse learning mechanism .Individual evolution mode is im‐proved by density diversity of the optimal values of all groups which are calculated through dynamic change the multiplicity ratio .The simulation results show that the proposed algorithm has fine capability of finding global optimum .
Keywords：shuffled flog leaping algorithmopposition-based learningdiversityoptimization
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 1175-1177,1211 )