Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates
This paper proposes a Gaussian process (GP) based co-sub-Pareto front surrogate augmentation strategy for evolutionary optimization of computationally expensive multiobjective problems. In the proposed algorithm, a multiobjective problem is decomposed into a number of subproblems, the solution of ea...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/150433 |
| _version_ | 1811681413684002816 |
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| author | Luo, Jianping Gupta, Abhishek Ong, Yew-Soon Wang, Zhenkun |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Luo, Jianping Gupta, Abhishek Ong, Yew-Soon Wang, Zhenkun |
| author_sort | Luo, Jianping |
| collection | NTU |
| description | This paper proposes a Gaussian process (GP) based co-sub-Pareto front surrogate augmentation strategy for evolutionary optimization of computationally expensive multiobjective problems. In the proposed algorithm, a multiobjective problem is decomposed into a number of subproblems, the solution of each of which is used to approximate a portion or sector of the Pareto front (i.e., a subPF). Thereafter, a multitask GP model is incorporated to exploit the correlations across the subproblems via joint surrogate model learning. A novel criterion for the utility function is defined on the surrogate landscape to determine the next candidate solution for evaluation using the actual expensive objectives. In addition, a new management strategy for the evaluated solutions is presented for model building. The novel feature of our approach is that it infers multiple subproblems jointly by exploiting the possible dependencies between them, such that knowledge can be transferred across subPFs approximated by the subproblems. Experimental studies under several scenarios indicate that the proposed algorithm outperforms state-of-the-art multiobjective evolutionary algorithms for expensive problems. The parameter sensitivity and effectiveness of the proposed algorithm are analyzed in detail. |
| first_indexed | 2024-10-01T03:40:33Z |
| format | Journal Article |
| id | ntu-10356/150433 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:40:33Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1504332021-05-31T01:38:52Z Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates Luo, Jianping Gupta, Abhishek Ong, Yew-Soon Wang, Zhenkun School of Computer Science and Engineering Data Science and Artificial Intelligence Research Centre Air Traffic Management Research Institute Engineering::Computer science and engineering Expensive Optimization Multiobjective Evolutionary Algorithm (EA) This paper proposes a Gaussian process (GP) based co-sub-Pareto front surrogate augmentation strategy for evolutionary optimization of computationally expensive multiobjective problems. In the proposed algorithm, a multiobjective problem is decomposed into a number of subproblems, the solution of each of which is used to approximate a portion or sector of the Pareto front (i.e., a subPF). Thereafter, a multitask GP model is incorporated to exploit the correlations across the subproblems via joint surrogate model learning. A novel criterion for the utility function is defined on the surrogate landscape to determine the next candidate solution for evaluation using the actual expensive objectives. In addition, a new management strategy for the evaluated solutions is presented for model building. The novel feature of our approach is that it infers multiple subproblems jointly by exploiting the possible dependencies between them, such that knowledge can be transferred across subPFs approximated by the subproblems. Experimental studies under several scenarios indicate that the proposed algorithm outperforms state-of-the-art multiobjective evolutionary algorithms for expensive problems. The parameter sensitivity and effectiveness of the proposed algorithm are analyzed in detail. 2021-05-31T01:38:51Z 2021-05-31T01:38:51Z 2018 Journal Article Luo, J., Gupta, A., Ong, Y. & Wang, Z. (2018). Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates. IEEE Transactions On Cybernetics, 49(5), 1708-1721. https://dx.doi.org/10.1109/TCYB.2018.2811761 2168-2267 https://hdl.handle.net/10356/150433 10.1109/TCYB.2018.2811761 29993877 2-s2.0-85043776418 5 49 1708 1721 en IEEE Transactions on Cybernetics © 2018 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Expensive Optimization Multiobjective Evolutionary Algorithm (EA) Luo, Jianping Gupta, Abhishek Ong, Yew-Soon Wang, Zhenkun Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates |
| title | Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates |
| title_full | Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates |
| title_fullStr | Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates |
| title_full_unstemmed | Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates |
| title_short | Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates |
| title_sort | evolutionary optimization of expensive multiobjective problems with co sub pareto front gaussian process surrogates |
| topic | Engineering::Computer science and engineering Expensive Optimization Multiobjective Evolutionary Algorithm (EA) |
| url | https://hdl.handle.net/10356/150433 |
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