Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study
Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ameca-Alducin, María-Yaneli [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 22(2016), 2 vom: 27. Sept., Seite 541-570 |
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| Übergeordnetes Werk: |
volume:22 ; year:2016 ; number:2 ; day:27 ; month:09 ; pages:541-570 |
| Links: |
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| DOI / URN: |
10.1007/s00500-016-2353-1 |
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OLC2034888472 |
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| 520 | |a Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints. | ||
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10.1007/s00500-016-2353-1 doi (DE-627)OLC2034888472 (DE-He213)s00500-016-2353-1-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Ameca-Alducin, María-Yaneli verfasserin aut Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints. Differential evolution Constraint handling Dynamic optimization Dynamic constrained optimization problem Mezura-Montes, Efrén aut Cruz-Ramírez, Nicandro aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 22(2016), 2 vom: 27. Sept., Seite 541-570 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:22 year:2016 number:2 day:27 month:09 pages:541-570 https://doi.org/10.1007/s00500-016-2353-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 22 2016 2 27 09 541-570 |
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10.1007/s00500-016-2353-1 doi (DE-627)OLC2034888472 (DE-He213)s00500-016-2353-1-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Ameca-Alducin, María-Yaneli verfasserin aut Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints. Differential evolution Constraint handling Dynamic optimization Dynamic constrained optimization problem Mezura-Montes, Efrén aut Cruz-Ramírez, Nicandro aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 22(2016), 2 vom: 27. Sept., Seite 541-570 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:22 year:2016 number:2 day:27 month:09 pages:541-570 https://doi.org/10.1007/s00500-016-2353-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 22 2016 2 27 09 541-570 |
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10.1007/s00500-016-2353-1 doi (DE-627)OLC2034888472 (DE-He213)s00500-016-2353-1-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Ameca-Alducin, María-Yaneli verfasserin aut Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints. Differential evolution Constraint handling Dynamic optimization Dynamic constrained optimization problem Mezura-Montes, Efrén aut Cruz-Ramírez, Nicandro aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 22(2016), 2 vom: 27. Sept., Seite 541-570 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:22 year:2016 number:2 day:27 month:09 pages:541-570 https://doi.org/10.1007/s00500-016-2353-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 22 2016 2 27 09 541-570 |
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dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study |
| title_auth |
Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study |
| abstract |
Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints. © Springer-Verlag Berlin Heidelberg 2016 |
| abstractGer |
Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints. © Springer-Verlag Berlin Heidelberg 2016 |
| abstract_unstemmed |
Abstract An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints. © Springer-Verlag Berlin Heidelberg 2016 |
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Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study |
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Mezura-Montes, Efrén Cruz-Ramírez, Nicandro |
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