Adaptive Synchornized Factor Shuffled Frog Leaping Algorithm
LI Minnan
LIU Sheng
Abstract：Basic shuffled frog leaping algorithm(SFLA)has a slow convergence speed and a low precision. To overcome these shortcomings,this paper proposes an improved algorithm-adaptive synchronized factor shuffled frog leaping algorithm(AS_SFLA). In this algorithm,the adaptive synchronized factor is introduced to change frog update rule in local iterations to improve the ability in local search. Each species update according to the corresponding position updating formula. The factor disturbs the individual when the position updates,which increases the diversity of population location and adjusts the search scope. Each individual adjusts the factor dynamically in the local iterations. The rule of updating positions is more reasonable. Compared simulation results of exper?iments on nine benchmark functions with two different groups of factors among SFLA,AS_SFLA and ISFLA1,the results show that the adaptive synchronized factor strategy balances the searching ability of AS_SFLA in the local and global iteration processes, which makes the algorithm avoid to fall into local optimum. Finally,AS_SFLA is proved to act better in solution quality,searching ability and can be more suitable for high-dimensional optimization of complex functions.
Keywords：shuffled frog leaping algorithmadaptive synchronized factorupdate rulelocal searching strategyintra group iteration
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1083-1088 )