TY - JOUR
T1 - Theta-mechanism based cluster search algorithm for global constrained optimization
AU - Chen, Hao
AU - Jia, Fengzhu
AU - Pan, Xiaoying
AU - Wei, Zhi
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Cluster search algorithm
KW - Global constrained optimization
KW - Search reachability analysis
KW - Theta-mechanism
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U2 - 10.1016/j.asoc.2023.110963
DO - 10.1016/j.asoc.2023.110963
M3 - Article
AN - SCOPUS:85174840890
SN - 1568-4946
VL - 149
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 110963
ER -