An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions
Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorith...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fan, Zhun [verfasserIn] Li, Wenji [verfasserIn] Cai, Xinye [verfasserIn] Huang, Han [verfasserIn] Fang, Yi [verfasserIn] You, Yugen [verfasserIn] Mo, Jiajie [verfasserIn] Wei, Caimin [verfasserIn] Goodman, Erik [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Constrained multi-objective evolutionary algorithms |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 23(2019), 23 vom: 04. Feb., Seite 12491-12510 |
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| Übergeordnetes Werk: |
volume:23 ; year:2019 ; number:23 ; day:04 ; month:02 ; pages:12491-12510 |
| Links: |
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| DOI / URN: |
10.1007/s00500-019-03794-x |
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| Katalog-ID: |
SPR006509223 |
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| 245 | 1 | 3 | |a An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions |
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| 520 | |a Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. | ||
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| 650 | 4 | |a Robot gripper optimization |7 (dpeaa)DE-He213 | |
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10.1007/s00500-019-03794-x doi (DE-627)SPR006509223 (SPR)s00500-019-03794-x-e DE-627 ger DE-627 rakwb eng Fan, Zhun verfasserin aut An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. Constrained multi-objective evolutionary algorithms (dpeaa)DE-He213 Epsilon constraint handling (dpeaa)DE-He213 Constrained multi-objective optimization (dpeaa)DE-He213 Robot gripper optimization (dpeaa)DE-He213 Li, Wenji verfasserin aut Cai, Xinye verfasserin aut Huang, Han verfasserin aut Fang, Yi verfasserin aut You, Yugen verfasserin aut Mo, Jiajie verfasserin aut Wei, Caimin verfasserin aut Goodman, Erik verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 04. Feb., Seite 12491-12510 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:04 month:02 pages:12491-12510 https://dx.doi.org/10.1007/s00500-019-03794-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 04 02 12491-12510 |
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10.1007/s00500-019-03794-x doi (DE-627)SPR006509223 (SPR)s00500-019-03794-x-e DE-627 ger DE-627 rakwb eng Fan, Zhun verfasserin aut An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. Constrained multi-objective evolutionary algorithms (dpeaa)DE-He213 Epsilon constraint handling (dpeaa)DE-He213 Constrained multi-objective optimization (dpeaa)DE-He213 Robot gripper optimization (dpeaa)DE-He213 Li, Wenji verfasserin aut Cai, Xinye verfasserin aut Huang, Han verfasserin aut Fang, Yi verfasserin aut You, Yugen verfasserin aut Mo, Jiajie verfasserin aut Wei, Caimin verfasserin aut Goodman, Erik verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 04. Feb., Seite 12491-12510 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:04 month:02 pages:12491-12510 https://dx.doi.org/10.1007/s00500-019-03794-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 04 02 12491-12510 |
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10.1007/s00500-019-03794-x doi (DE-627)SPR006509223 (SPR)s00500-019-03794-x-e DE-627 ger DE-627 rakwb eng Fan, Zhun verfasserin aut An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. Constrained multi-objective evolutionary algorithms (dpeaa)DE-He213 Epsilon constraint handling (dpeaa)DE-He213 Constrained multi-objective optimization (dpeaa)DE-He213 Robot gripper optimization (dpeaa)DE-He213 Li, Wenji verfasserin aut Cai, Xinye verfasserin aut Huang, Han verfasserin aut Fang, Yi verfasserin aut You, Yugen verfasserin aut Mo, Jiajie verfasserin aut Wei, Caimin verfasserin aut Goodman, Erik verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 04. Feb., Seite 12491-12510 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:04 month:02 pages:12491-12510 https://dx.doi.org/10.1007/s00500-019-03794-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 04 02 12491-12510 |
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10.1007/s00500-019-03794-x doi (DE-627)SPR006509223 (SPR)s00500-019-03794-x-e DE-627 ger DE-627 rakwb eng Fan, Zhun verfasserin aut An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. Constrained multi-objective evolutionary algorithms (dpeaa)DE-He213 Epsilon constraint handling (dpeaa)DE-He213 Constrained multi-objective optimization (dpeaa)DE-He213 Robot gripper optimization (dpeaa)DE-He213 Li, Wenji verfasserin aut Cai, Xinye verfasserin aut Huang, Han verfasserin aut Fang, Yi verfasserin aut You, Yugen verfasserin aut Mo, Jiajie verfasserin aut Wei, Caimin verfasserin aut Goodman, Erik verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 04. Feb., Seite 12491-12510 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:04 month:02 pages:12491-12510 https://dx.doi.org/10.1007/s00500-019-03794-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 04 02 12491-12510 |
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10.1007/s00500-019-03794-x doi (DE-627)SPR006509223 (SPR)s00500-019-03794-x-e DE-627 ger DE-627 rakwb eng Fan, Zhun verfasserin aut An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. Constrained multi-objective evolutionary algorithms (dpeaa)DE-He213 Epsilon constraint handling (dpeaa)DE-He213 Constrained multi-objective optimization (dpeaa)DE-He213 Robot gripper optimization (dpeaa)DE-He213 Li, Wenji verfasserin aut Cai, Xinye verfasserin aut Huang, Han verfasserin aut Fang, Yi verfasserin aut You, Yugen verfasserin aut Mo, Jiajie verfasserin aut Wei, Caimin verfasserin aut Goodman, Erik verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 04. Feb., Seite 12491-12510 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:04 month:02 pages:12491-12510 https://dx.doi.org/10.1007/s00500-019-03794-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 04 02 12491-12510 |
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An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions Constrained multi-objective evolutionary algorithms (dpeaa)DE-He213 Epsilon constraint handling (dpeaa)DE-He213 Constrained multi-objective optimization (dpeaa)DE-He213 Robot gripper optimization (dpeaa)DE-He213 |
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misc Constrained multi-objective evolutionary algorithms misc Epsilon constraint handling misc Constrained multi-objective optimization misc Robot gripper optimization |
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misc Constrained multi-objective evolutionary algorithms misc Epsilon constraint handling misc Constrained multi-objective optimization misc Robot gripper optimization |
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misc Constrained multi-objective evolutionary algorithms misc Epsilon constraint handling misc Constrained multi-objective optimization misc Robot gripper optimization |
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An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions |
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An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions |
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Fan, Zhun |
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Soft Computing |
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Fan, Zhun Li, Wenji Cai, Xinye Huang, Han Fang, Yi You, Yugen Mo, Jiajie Wei, Caimin Goodman, Erik |
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10.1007/s00500-019-03794-x |
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improved epsilon constraint-handling method in moea/d for cmops with large infeasible regions |
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An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions |
| abstract |
Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. |
| abstractGer |
Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. |
| abstract_unstemmed |
Abstract This paper proposes an improved epsilon constraint-handling mechanism and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then, the 14 benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and CMOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs. The experimental results demonstrate that MOEA/D-IEpsilon also outperforms the other four CMOEAs on this problem. |
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An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions |
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