A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer
Fruit fly optimization algorithm (FOA) is one of the swarm intelligence algorithms proposed for solving continuous optimization problems. In the basic FOA, the best solution is always taken into consideration by the other artificial fruit flies when solving the problem. This behavior of FOA causes g...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8827461/ |
| _version_ | 1811273959301185536 |
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| author | Hazim Iscan Mustafa Servet Kiran Mesut Gunduz |
| author_facet | Hazim Iscan Mustafa Servet Kiran Mesut Gunduz |
| author_sort | Hazim Iscan |
| collection | DOAJ |
| description | Fruit fly optimization algorithm (FOA) is one of the swarm intelligence algorithms proposed for solving continuous optimization problems. In the basic FOA, the best solution is always taken into consideration by the other artificial fruit flies when solving the problem. This behavior of FOA causes getting trap into local minima because the whole population become very similar to each other and the best solution in the population during the search. Moreover, the basic FOA searches the positive side of solution space of the optimization problem. In order to overcome these issues, this study presents two novel versions of FOA, pFOA_v1 and pFOA_v2 for short, that take into account not only the best solutions but also the worst solutions during the search. Therefore, the proposed approaches aim to improve the FOA’s performance in solving continuous optimizations by removing these disadvantages. In order to investigate the performance of the novel proposed FOA versions, 21 well-known numeric benchmark functions are considered in the experiments. The obtained experimental results of pFOA versions have been compared with the basic FOA, SFOA which is an improved version of basic FOA, SPSO2011 which is one of the latest versions of particle swarm optimization, firefly algorithm called FA, tree seed algorithm TSA for short, cuckoo search algorithm briefly CS, and a new optimization algorithm JAYA. The experimental results and comparisons show that the proposed versions of FOA are better than the basic FOA and SFOA, and produce comparable and competitive results for the continuous optimization problems. |
| first_indexed | 2024-04-12T23:10:09Z |
| format | Article |
| id | doaj.art-23b3151fd0f7487f8284cdd4cb8b4b61 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:10:09Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-23b3151fd0f7487f8284cdd4cb8b4b612022-12-22T03:12:49ZengIEEEIEEE Access2169-35362019-01-01713090313092110.1109/ACCESS.2019.29401048827461A Novel Candidate Solution Generation Strategy for Fruit Fly OptimizerHazim Iscan0https://orcid.org/0000-0002-3698-3745Mustafa Servet Kiran1Mesut Gunduz2Department of Computer Engineering, Faculty of Engineering, Selcuk University, Konya, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Selcuk University, Konya, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Selcuk University, Konya, TurkeyFruit fly optimization algorithm (FOA) is one of the swarm intelligence algorithms proposed for solving continuous optimization problems. In the basic FOA, the best solution is always taken into consideration by the other artificial fruit flies when solving the problem. This behavior of FOA causes getting trap into local minima because the whole population become very similar to each other and the best solution in the population during the search. Moreover, the basic FOA searches the positive side of solution space of the optimization problem. In order to overcome these issues, this study presents two novel versions of FOA, pFOA_v1 and pFOA_v2 for short, that take into account not only the best solutions but also the worst solutions during the search. Therefore, the proposed approaches aim to improve the FOA’s performance in solving continuous optimizations by removing these disadvantages. In order to investigate the performance of the novel proposed FOA versions, 21 well-known numeric benchmark functions are considered in the experiments. The obtained experimental results of pFOA versions have been compared with the basic FOA, SFOA which is an improved version of basic FOA, SPSO2011 which is one of the latest versions of particle swarm optimization, firefly algorithm called FA, tree seed algorithm TSA for short, cuckoo search algorithm briefly CS, and a new optimization algorithm JAYA. The experimental results and comparisons show that the proposed versions of FOA are better than the basic FOA and SFOA, and produce comparable and competitive results for the continuous optimization problems.https://ieeexplore.ieee.org/document/8827461/Fruit fly algorithmbest-worst strategycontinuous optimizationnumeric benchmark problem |
| spellingShingle | Hazim Iscan Mustafa Servet Kiran Mesut Gunduz A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer IEEE Access Fruit fly algorithm best-worst strategy continuous optimization numeric benchmark problem |
| title | A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer |
| title_full | A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer |
| title_fullStr | A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer |
| title_full_unstemmed | A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer |
| title_short | A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer |
| title_sort | novel candidate solution generation strategy for fruit fly optimizer |
| topic | Fruit fly algorithm best-worst strategy continuous optimization numeric benchmark problem |
| url | https://ieeexplore.ieee.org/document/8827461/ |
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