Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification
In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack o...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/3/707 |
| _version_ | 1797623848373321728 |
|---|---|
| author | Essam H. Houssein Awny Sayed |
| author_facet | Essam H. Houssein Awny Sayed |
| author_sort | Essam H. Houssein |
| collection | DOAJ |
| description | In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO’s Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC’22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix. |
| first_indexed | 2024-03-11T09:34:38Z |
| format | Article |
| id | doaj.art-ad58842b454546cb905a946ae80964d6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:34:38Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ad58842b454546cb905a946ae80964d62023-11-16T17:23:26ZengMDPI AGMathematics2227-73902023-01-0111370710.3390/math11030707Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical ClassificationEssam H. Houssein0Awny Sayed1Faculty of Computers and Information, Minia University, Minia 61519, EgyptInformation Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaIn many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO’s Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC’22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix.https://www.mdpi.com/2227-7390/11/3/707Artificial Intelligence (AI)Beluga Whale Optimization (BWO)Dynamic Candidate Solution (DCS)Opposition-Based Learning (OBL)k-Nearest Neighbor (kNN) |
| spellingShingle | Essam H. Houssein Awny Sayed Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification Mathematics Artificial Intelligence (AI) Beluga Whale Optimization (BWO) Dynamic Candidate Solution (DCS) Opposition-Based Learning (OBL) k-Nearest Neighbor (kNN) |
| title | Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification |
| title_full | Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification |
| title_fullStr | Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification |
| title_full_unstemmed | Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification |
| title_short | Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification |
| title_sort | dynamic candidate solution boosted beluga whale optimization algorithm for biomedical classification |
| topic | Artificial Intelligence (AI) Beluga Whale Optimization (BWO) Dynamic Candidate Solution (DCS) Opposition-Based Learning (OBL) k-Nearest Neighbor (kNN) |
| url | https://www.mdpi.com/2227-7390/11/3/707 |
| work_keys_str_mv | AT essamhhoussein dynamiccandidatesolutionboostedbelugawhaleoptimizationalgorithmforbiomedicalclassification AT awnysayed dynamiccandidatesolutionboostedbelugawhaleoptimizationalgorithmforbiomedicalclassification |