Theta-mechanism based cluster search algorithm for global constrained optimization
Document Type
Article
Publication Date
12-1-2023
Abstract
This study concerns constructing an evolutionary search system to solve the global constrained optimization problems. Firstly, we proposed a hybrid constraint-handling method, called theta-mechanism, which blends two types of constraint-handling functions and alternates use of them in the searching process to balance two competing objectives: seeking as much as possible feasible regions and quickly converging to the optimum point in the found feasible regions. Secondly, to enable the search system to cooperate well with the theta-mechanism, we designed the cluster search algorithm (CSA) and developed the search reachability analysis (SRA) method. Based on SRA, we evaluated the characteristics of several typical search operators in order to assemble them into different operator combinations in CSA to maximize its performance, which enables CSA with theta-mechanism to accomplish the two inconsistent search objectives effectively. We tested the proposed method on 18 benchmark functions from IEEE CEC2010 and 32 real-world constrained optimization problems collected in IEEE CEC2020. Our results show the CSA with theta-mechanism is more competitive than the existing state-of-the-art approaches.
Identifier
85174840890 (Scopus)
Publication Title
Applied Soft Computing
External Full Text Location
https://doi.org/10.1016/j.asoc.2023.110963
ISSN
15684946
Volume
149
Grant
61203311
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Chen, Hao; Jia, Fengzhu; Pan, Xiaoying; and Wei, Zhi, "Theta-mechanism based cluster search algorithm for global constrained optimization" (2023). Faculty Publications. 1260.
https://digitalcommons.njit.edu/fac_pubs/1260