Local Model-Based Pareto Front Estimation for Multiobjective Optimization

Tian Y, Si L, Zhang X, Tan KC, Jin Y (2022)
IEEE Transactions on Systems, Man, and Cybernetics: Systems .

Zeitschriftenaufsatz | E-Veröff. vor dem Druck | Englisch
 
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Autor*in
Tian, Ye; Si, Langchun; Zhang, Xingyi; Tan, Kay Chen; Jin, YaochuUniBi
Abstract / Bemerkung
The Pareto front (PF) estimation has become an emerging strategy for solving multiobjective optimization problems in recent studies. By approximating the geometrical structure of the PF during the evolutionary procedure, some PF estimation approaches have been suggested and shown effectiveness in guiding the search direction of evolutionary algorithms. However, these approaches encounter difficulties in handling irregular PFs, whose geometrical structures are too complex to be properly approximated. To address this issue, this article proposes a novel PF estimation approach based on local models. In contrast to existing approaches estimating the PF via a reference point set or a single model, the proposed approach automatically divides the population into several groups and builds a local model for each group of solutions. In spite of the simplicity of each local model, the combination of all the local models can approximate the PFs with complex geometrical structures. An evolutionary algorithm is then developed based on the local model-based PF estimation approach and a novel fitness function and is compared with four evolutionary algorithms on 39 problems. Statistical results indicate that the proposed algorithm exhibits better performance than the compared algorithms, especially on problems with highly irregular PFs.
Stichworte
Statistics; Sociology; Estimation; Manifolds; Optimization; Evolutionary; computation; Mathematical models; Clustering; evolutionary; multiobjective optimization; local model; Pareto front (PF) estimation
Erscheinungsjahr
2022
Zeitschriftentitel
IEEE Transactions on Systems, Man, and Cybernetics: Systems
ISSN
2168-2216
eISSN
2168-2232
Page URI
https://pub.uni-bielefeld.de/record/2964655

Zitieren

Tian Y, Si L, Zhang X, Tan KC, Jin Y. Local Model-Based Pareto Front Estimation for Multiobjective Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems . 2022.
Tian, Y., Si, L., Zhang, X., Tan, K. C., & Jin, Y. (2022). Local Model-Based Pareto Front Estimation for Multiobjective Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems . https://doi.org/10.1109/TSMC.2022.3186546
Tian, Y., Si, L., Zhang, X., Tan, K. C., and Jin, Y. (2022). Local Model-Based Pareto Front Estimation for Multiobjective Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems .
Tian, Y., et al., 2022. Local Model-Based Pareto Front Estimation for Multiobjective Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems .
Y. Tian, et al., “Local Model-Based Pareto Front Estimation for Multiobjective Optimization”, IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2022.
Tian, Y., Si, L., Zhang, X., Tan, K.C., Jin, Y.: Local Model-Based Pareto Front Estimation for Multiobjective Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems . (2022).
Tian, Ye, Si, Langchun, Zhang, Xingyi, Tan, Kay Chen, and Jin, Yaochu. “Local Model-Based Pareto Front Estimation for Multiobjective Optimization”. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2022).

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