Multi-stage dimension reduction for expensive sparse multi-objective optimization problems
A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evol...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tan, Zheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Umfang: |
16 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:440 ; year:2021 ; day:14 ; month:06 ; pages:159-174 ; extent:16 |
| Links: |
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| DOI / URN: |
10.1016/j.neucom.2021.01.115 |
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ELV053738276 |
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| 520 | |a A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. | ||
| 520 | |a A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. | ||
| 650 | 7 | |a Dimension reduction |2 Elsevier | |
| 650 | 7 | |a Surrogate-assisted evolutionary algorithms |2 Elsevier | |
| 650 | 7 | |a Sparse multi-objective optimization problems |2 Elsevier | |
| 700 | 1 | |a Wang, Handing |4 oth | |
| 700 | 1 | |a Liu, Shulei |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
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10.1016/j.neucom.2021.01.115 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001367.pica (DE-627)ELV053738276 (ELSEVIER)S0925-2312(21)00216-2 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Tan, Zheng verfasserin aut Multi-stage dimension reduction for expensive sparse multi-objective optimization problems 2021transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. Dimension reduction Elsevier Surrogate-assisted evolutionary algorithms Elsevier Sparse multi-objective optimization problems Elsevier Wang, Handing oth Liu, Shulei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:440 year:2021 day:14 month:06 pages:159-174 extent:16 https://doi.org/10.1016/j.neucom.2021.01.115 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 440 2021 14 0614 159-174 16 |
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10.1016/j.neucom.2021.01.115 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001367.pica (DE-627)ELV053738276 (ELSEVIER)S0925-2312(21)00216-2 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Tan, Zheng verfasserin aut Multi-stage dimension reduction for expensive sparse multi-objective optimization problems 2021transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. Dimension reduction Elsevier Surrogate-assisted evolutionary algorithms Elsevier Sparse multi-objective optimization problems Elsevier Wang, Handing oth Liu, Shulei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:440 year:2021 day:14 month:06 pages:159-174 extent:16 https://doi.org/10.1016/j.neucom.2021.01.115 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 440 2021 14 0614 159-174 16 |
| allfields_unstemmed |
10.1016/j.neucom.2021.01.115 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001367.pica (DE-627)ELV053738276 (ELSEVIER)S0925-2312(21)00216-2 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Tan, Zheng verfasserin aut Multi-stage dimension reduction for expensive sparse multi-objective optimization problems 2021transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. Dimension reduction Elsevier Surrogate-assisted evolutionary algorithms Elsevier Sparse multi-objective optimization problems Elsevier Wang, Handing oth Liu, Shulei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:440 year:2021 day:14 month:06 pages:159-174 extent:16 https://doi.org/10.1016/j.neucom.2021.01.115 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 440 2021 14 0614 159-174 16 |
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10.1016/j.neucom.2021.01.115 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001367.pica (DE-627)ELV053738276 (ELSEVIER)S0925-2312(21)00216-2 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Tan, Zheng verfasserin aut Multi-stage dimension reduction for expensive sparse multi-objective optimization problems 2021transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. Dimension reduction Elsevier Surrogate-assisted evolutionary algorithms Elsevier Sparse multi-objective optimization problems Elsevier Wang, Handing oth Liu, Shulei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:440 year:2021 day:14 month:06 pages:159-174 extent:16 https://doi.org/10.1016/j.neucom.2021.01.115 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 440 2021 14 0614 159-174 16 |
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10.1016/j.neucom.2021.01.115 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001367.pica (DE-627)ELV053738276 (ELSEVIER)S0925-2312(21)00216-2 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Tan, Zheng verfasserin aut Multi-stage dimension reduction for expensive sparse multi-objective optimization problems 2021transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. Dimension reduction Elsevier Surrogate-assisted evolutionary algorithms Elsevier Sparse multi-objective optimization problems Elsevier Wang, Handing oth Liu, Shulei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:440 year:2021 day:14 month:06 pages:159-174 extent:16 https://doi.org/10.1016/j.neucom.2021.01.115 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 440 2021 14 0614 159-174 16 |
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Multi-stage dimension reduction for expensive sparse multi-objective optimization problems |
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A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. |
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A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. |
| abstract_unstemmed |
A number of sparse multi-objective optimization problems (SMOPs) exist in the real world. Decision variables in their Pareto optimal solutions are not only large-scale but also very sparse, most decision variables are zero, which poses difficulties for the optimization. Existing multi-objective evolutionary algorithms need many function evaluations to handle the large number of decision variables, which is not available for the real-world problems with expensive function evaluations. However, applying surrogate-assisted evolutionary algorithms (SAEAs), proposed for expensive problems, to SMOPs also often falls into the curse of dimensionality. To deal with the dilemma, we propose a multi-stage dimension reduction method for expensive SMOPs to make SAEAs capable to handle. A non-dominated sorting based feature selection is executed by assessing each decision variable independently in the first stage. The sparsity and the specific non-zero decision variables are adaptively determined in an evolutionary process and the dimension of the problem is further reduced accordingly. Then the number of dimension-reduced subproblems are determined by an estimation of the potential calculation cost based on the determined sparsity and non-zero decision variables. Then, an SAEA is adopted for these dimension-reduced subproblems. Each optimal solution obtained is supplemented with a certain number of zero to ensure that its dimension is consistent with the original problem. The number of function evaluations required for each problem is affected by the varying decision variables in the dimension reduction process, so the cost of the proposed algorithm is determined adaptively in different problems. Experiment results on a test suite and one application problem show that the proposed algorithm achieves good performance on SMOPs in the case of limited computation budget. |
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