Open AccessOptimization of CSO algorithm based on adaptive inertia weight coefficient
Author(s) -
Dongnan Suo
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012009
Subject(s) - inertia , crossover , convergence (economics) , algorithm , position (finance) , range (aeronautics) , mathematics , mathematical optimization , mutation , value (mathematics) , computer science , artificial intelligence , statistics , engineering , biochemistry , chemistry , physics , finance , classical mechanics , aerospace engineering , economics , gene , economic growth
The traditional CSO algorithm is easy to fall into local extremum in optimization. In this paper, a CSO algorithm based on weight coefficient is proposed. In the CSO algorithm, the inertia weight coefficient is introduced into the hen position formula, and the learning factor influenced by the rooster is added to the chick position formula. Finally, using the idea of heredity, individuals with excellent fitness value are selected for crossover and mutation with a certain probability. Through the simulation comparison of five typical test functions, the simulation results show that the improved CSO algorithm can avoid local optimization, strengthen the global extreme value search ability, and improve the convergence speed and accuracy range of the algorithm.