Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation

Huang, Changwu1; Li, Lianghao2; He, Cheng1; Cheng, Ran1; Yao, Xin1

2022-05-01

10.1007/s12293-022-00368-7

Memetic Computing   影响因子和分区

1865-9284

1865-9292

As a typical large-scale multiobjective optimization problem extracted from real-world applications, the voltage transformer ratio error estimation (TREE) problem is challenging for existing evolutionary algorithms (EAs). Due to the large number of decision variables in the problems, existing algorithms cannot solve TREE problems efficiently. Besides, most EAs may fail to balance the convergence enhancement and diversity maintenance, leading to the trap in local optima even at the early stage of the evolution. This work proposes an adaptive large-scale multiobjective EA (LSMOEA) to handle the TREE problems with thousands of decision variables. Generally, multiple efficient offspring generation and environmental selection strategies selected from some representative LSMOEAs are included. Then an adaptive selection strategy is used to determine which offspring generation and environmental selection operators are used in each generation of the evolution. Thus, the search behavior of the proposed algorithm evolves along with the evolution process, the balance between convergence and diversity is maintained, and the proposed algorithm is expected to solve TREE problems effectively and efficiently. Experimental results show that the proposed algorithm achieves significant performance improvement due to the adaptive selection of different operators, providing an effective and efficient approach for large-scale optimization problems.

Large-scale optimization Multiobjective optimization Adaptive operator selection Voltage transformer ratio error estimation

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China["U20A20306",61903178,61906081] ; Guangdong Basic and Applied Basic Research Foundation[2019A1515110575] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Shenzhen Science and Technology Program["KQTD2016112514355531","RCBS20200714114817264"]

Computer Science ; Operations Research & Management Science

Computer Science, Artificial Intelligence ; Operations Research & Management Science

WOS:000790694500001

SPRINGER HEIDELBERG

20221912094186

Decision making ; Errors ; Evolutionary algorithms ; Forestry ; Power transformers ; Trees (mathematics)

Electric Power Lines and Equipment:706.2 ; Woodlands and Forestry:821.0 ; Management:912.2 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China
2.Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Image Informat Proc & Intelligent Control, Wuhan 430074, Peoples R China

Huang, Changwu,Li, Lianghao,He, Cheng,et al. Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation[J]. Memetic Computing,2022,14:237-251.

Huang, Changwu,Li, Lianghao,He, Cheng,Cheng, Ran,&Yao, Xin.(2022).Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation.Memetic Computing,14,237-251.

Huang, Changwu,et al."Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation".Memetic Computing 14(2022):237-251.