An improved bees algorithm local search mechanism for numerical dataset
Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA i...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | en en |
| Published: |
2015
|
| Subjects: | |
| Online Access: | https://etd.uum.edu.my/5622/1/s813731_01.pdf https://etd.uum.edu.my/5622/2/s813731_02.pdf https://etd.uum.edu.my/5622/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833436548913168384 |
|---|---|
| author | Al-Dawoodi, Aras Ghazi Mohammed |
| author_facet | Al-Dawoodi, Aras Ghazi Mohammed |
| author_sort | Al-Dawoodi, Aras Ghazi Mohammed |
| building | UUM Library |
| collection | Institutional Repository |
| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| description | Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any
optimum solutions for the problem mainly because of lack of accuracy. To solve this
issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this
study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods. |
| format | Thesis |
| id | my.uum.etd-5622 |
| institution | Universiti Utara Malaysia |
| language | en en |
| publishDate | 2015 |
| record_format | eprints |
| spelling | my.uum.etd-56222021-03-18T03:08:52Z https://etd.uum.edu.my/5622/ An improved bees algorithm local search mechanism for numerical dataset Al-Dawoodi, Aras Ghazi Mohammed QA75 Electronic computers. Computer science Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods. 2015 Thesis NonPeerReviewed text en https://etd.uum.edu.my/5622/1/s813731_01.pdf text en https://etd.uum.edu.my/5622/2/s813731_02.pdf Al-Dawoodi, Aras Ghazi Mohammed (2015) An improved bees algorithm local search mechanism for numerical dataset. Masters thesis, Universiti Utara Malaysia. |
| spellingShingle | QA75 Electronic computers. Computer science Al-Dawoodi, Aras Ghazi Mohammed An improved bees algorithm local search mechanism for numerical dataset |
| title | An improved bees algorithm local search mechanism for numerical dataset |
| title_full | An improved bees algorithm local search mechanism for numerical dataset |
| title_fullStr | An improved bees algorithm local search mechanism for numerical dataset |
| title_full_unstemmed | An improved bees algorithm local search mechanism for numerical dataset |
| title_short | An improved bees algorithm local search mechanism for numerical dataset |
| title_sort | improved bees algorithm local search mechanism for numerical dataset |
| topic | QA75 Electronic computers. Computer science |
| url | https://etd.uum.edu.my/5622/1/s813731_01.pdf https://etd.uum.edu.my/5622/2/s813731_02.pdf https://etd.uum.edu.my/5622/ |
| url_provider | http://etd.uum.edu.my/ |