Improved Slime Mold Algorithm with Multiple Strategies
WANG Xiaolei
PANG Na
LIU Libo
Abstract：The slime mold algorithm is easy to fall into local optimal stagnation and slow convergence speed,so an improved slime mold algorithm based on a variety of hybrid strategies is proposed.Firstly,the chaotic map is used to initialize the population and increase the diversity of the population.The global exploration and local development ability of the adaptive adjustable feedback factor coordination algorithm is introduced into the updating position of myxomycetes.The random learning strategy in the teaching and learning optimization algorithm is combined with the slime mold algorithm to avoid the blind optimization in the global algo-rithm.The mutation operation of Lévy flight mutation mechanism makes the algorithm jump out of local optimum.The performance of the improved algorithm is tested on eight standard test functions.The results show that the improved algorithm is robust,precise and fast.The classical optimization problem of truss structure is solved by the algorithm,which is superior to other algorithms in the opti-mization design of truss structure and runs fewer iterations to reach the objective function.
Keywords：slime molds algorithmchaotic mapback feed factorrandom learning strategyLévy flighttest functiontruss optimization
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 308-313,357 )