TY - JOUR
T1 - Cellular Genetic Algorithm with Density Dependence for Dynamic Optimization Problems
AU - Hao Chen, Ming Li and Xi Chen
JO - Journal of Information and Computing Science
VL - 4
SP - 287
EP - 298
PY - 2024
DA - 2024/01
SN - 5
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22703.html
KW - cellular genetic algorithm, dynamic optimization, density dependence scheme 

AB -  For  dynamic  optimization  problems,  the  aim  of  an  effective  optimization  algorithm  is  both  to 
find the optimal solutions and to track the optima over time. In this paper, we advanced two kinds of cellular 
genetic  algorithms  inspired  by  the  density  dependence  scheme  in  ecological  system  to  solving  dynamic 
optimization  problems.  Two  kinds  of  improved  evolution  rules  are  proposed  to  replace  the  rule  in  regular 
cellular genetic algorithm, in which null cells are considered to the foods of individuals in population and the 
maximum of living individuals in cellular space is limited by their food. Moreover, in the second proposed 
rule,  the  competition  scheme  of  the  best  individuals  within  the  neighborhoods  of  one  individual  is  also 
introduced.  The  performance  of  proposed  cellular  genetic  algorithms  is  examined  under  three  dynamic 
optimization problems with different change severities. The computation results indicate that new algorithms 
demonstrate their superiority respectively on both convergence and diversity.