TY - GEN
T1 - Population Sizing of Evolutionary Large-Scale Multiobjective Optimization
AU - He, Cheng
AU - Cheng, Ran
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Large-scale multiobjective optimization problems (LSMOPs) are emerging and widely existed in real-world applications, which involve a large number of decision variables and multiple conflicting objectives. Evolutionary algorithms (EAs) are naturally suitable for multiobjective optimization due to their population-based property, allowing the search of optima simultaneously. Nevertheless, LSMOPs are challenging for conventional EAs, mainly due to the huge volume of search space in LSMOPs. Thus, it is important to explore the impact of the population sizing on the performance of conventional multiobjective EAs (MOEAs) in solving LSMOPs. In this work, we compare several representative MOEAs with different settings of population sizes on some transformer ratio error estimation (TREE) problems in the power system. These test cases are defined on combinations of three population sizes, three TREE problems, and five MOEAs. Our results indicate that the performances of conventional MOEAs with different population sizes in solving LSMOPs are different. The impact of population sizing is most significant for differential evolution based and particle swarm based MOEAs.
AB - Large-scale multiobjective optimization problems (LSMOPs) are emerging and widely existed in real-world applications, which involve a large number of decision variables and multiple conflicting objectives. Evolutionary algorithms (EAs) are naturally suitable for multiobjective optimization due to their population-based property, allowing the search of optima simultaneously. Nevertheless, LSMOPs are challenging for conventional EAs, mainly due to the huge volume of search space in LSMOPs. Thus, it is important to explore the impact of the population sizing on the performance of conventional multiobjective EAs (MOEAs) in solving LSMOPs. In this work, we compare several representative MOEAs with different settings of population sizes on some transformer ratio error estimation (TREE) problems in the power system. These test cases are defined on combinations of three population sizes, three TREE problems, and five MOEAs. Our results indicate that the performances of conventional MOEAs with different population sizes in solving LSMOPs are different. The impact of population sizing is most significant for differential evolution based and particle swarm based MOEAs.
KW - Large-scale optimization
KW - Multiobjective optimization
KW - Population size
KW - Transformer ratio error estimation
UR - https://www.scopus.com/pages/publications/85107309719
U2 - 10.1007/978-3-030-72062-9_4
DO - 10.1007/978-3-030-72062-9_4
M3 - Conference article published in proceeding or book
AN - SCOPUS:85107309719
SN - 9783030720612
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 52
BT - Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings
A2 - Ishibuchi, Hisao
A2 - Zhang, Qingfu
A2 - Cheng, Ran
A2 - Li, Ke
A2 - Li, Hui
A2 - Wang, Handing
A2 - Zhou, Aimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021
Y2 - 28 March 2021 through 31 March 2021
ER -