Firefly Algorithm Based on Improved Evolutionary Model and Chaos Optimization
LI Zhaoji
CHENG Ke
WANG Wanyao
CUI Qinghua
Abstract：In order to overcome the disadvantages of firefly algorithm such as slow convergence speed,low computational ac?curacy and high possibility of being trapped in local optimum,a chaotic population firefly algorithm based on new evolutionary mod?el is proposed. Firstly,chaotic sequence generated by the logical self-mapping is used to initialize individual position,which lays the foundation for the diversity of the population. Secondly,the inertia weight is used to control the influence of the previous genera?tion position,and the population optimal individual is used to enhance the exchange of information between fireflies. Finally,the dy?namic step size and symmetric boundary variation operations is introduced to control cross-border problems and improve the diversi?ty of the population. The proposed algorithm is compared with standard firefly algorithm and firefly algorithm based on improved evo?lutionism on six benchmarks,and the results show that the proposed algorithm can not only obtain better solution accuracy and quicker convergence speed,but also avoid trapping in local optimum.
Keywords：firefly optimizationchaotic populationinertia weightevolutionary modeldynamic stepboundary mutation
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1605-1612 )