How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic
Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been sign...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/18/2189 |
| _version_ | 1797518354436587520 |
|---|---|
| author | Tania Pencheva Maria Angelova Evdokia Sotirova Krassimir Atanassov |
| author_facet | Tania Pencheva Maria Angelova Evdokia Sotirova Krassimir Atanassov |
| author_sort | Tania Pencheva |
| collection | DOAJ |
| description | Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a <i>S. cerevisiae</i> fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms. |
| first_indexed | 2024-03-10T07:28:43Z |
| format | Article |
| id | doaj.art-1be290094df740259b05d77e9ee15758 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T07:28:43Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-1be290094df740259b05d77e9ee157582023-11-22T14:04:38ZengMDPI AGMathematics2227-73902021-09-01918218910.3390/math9182189How to Assess Different Algorithms Using Intuitionistic Fuzzy LogicTania Pencheva0Maria Angelova1Evdokia Sotirova2Krassimir Atanassov3Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1113 Sofia, BulgariaInstitute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1113 Sofia, BulgariaLaboratory of Intelligent Systems, Faculty of Public Health and Health Care, Burgas University, 1, 8010 Burgas, BulgariaInstitute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1113 Sofia, BulgariaIntuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a <i>S. cerevisiae</i> fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms.https://www.mdpi.com/2227-7390/9/18/2189intuitionistic fuzzy logicgenetic algorithmsmodellingoptimizationfed-batch cultivation |
| spellingShingle | Tania Pencheva Maria Angelova Evdokia Sotirova Krassimir Atanassov How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic Mathematics intuitionistic fuzzy logic genetic algorithms modelling optimization fed-batch cultivation |
| title | How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic |
| title_full | How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic |
| title_fullStr | How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic |
| title_full_unstemmed | How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic |
| title_short | How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic |
| title_sort | how to assess different algorithms using intuitionistic fuzzy logic |
| topic | intuitionistic fuzzy logic genetic algorithms modelling optimization fed-batch cultivation |
| url | https://www.mdpi.com/2227-7390/9/18/2189 |
| work_keys_str_mv | AT taniapencheva howtoassessdifferentalgorithmsusingintuitionisticfuzzylogic AT mariaangelova howtoassessdifferentalgorithmsusingintuitionisticfuzzylogic AT evdokiasotirova howtoassessdifferentalgorithmsusingintuitionisticfuzzylogic AT krassimiratanassov howtoassessdifferentalgorithmsusingintuitionisticfuzzylogic |