Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem
The performance of meta-heuristic search algorithms highly depends on their intensification and diversification abilities. Different algorithms adopt intensification and diversification strategies in order to obtain better results. Elitism and mutation are common operators that are used for increasi...
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| Language: | English |
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American Scientific Publisher
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19740/ http://umpir.ump.edu.my/id/eprint/19740/1/32.%20Comparative%20Study%20of%20Adaptive%20Elitism%20and%20Mutation%20Operators%20in%20Flower%20Pollination%20Algorithm%20for%20Combinatorial%20Testing1.pdf |
| _version_ | 1848820952335384576 |
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| author | Abdullah, Nasser Kamal Z., Zamli |
| author_facet | Abdullah, Nasser Kamal Z., Zamli |
| author_sort | Abdullah, Nasser |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The performance of meta-heuristic search algorithms highly depends on their intensification and diversification abilities. Different algorithms adopt intensification and diversification strategies in order to obtain better results. Elitism and mutation are common operators that are used for increasing the diversity of the population. Flower Pollination Algorithm (FPA) is one of the recent meta-heuristic algorithms for global optimization. Although proven to be efficient, FPA is prone to get stuck into a local optimum due to the weakness of its population’s diversity especially for multimodal optimization problem. In this paper, first, we propose two strategies based on mutation-FPA (mFPA) and elitism-FPA (eFPA) for t-way test generation (t refer to interaction strength). Then, a comparison between mFPA and eFPA is studied to analysis the effect of introducing elitism and mutation operators on FPA’s performance. The results of the experiments show that both of eFPA and mFPA strategies appear to produce better results than original FPA strategy, however, eFPA performs much better than mFPA in term of tests size. |
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| format | Article |
| id | ump-19740 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
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| publishDate | 2018 |
| publisher | American Scientific Publisher |
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| spelling | ump-197402018-11-13T02:26:34Z http://umpir.ump.edu.my/id/eprint/19740/ Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem Abdullah, Nasser Kamal Z., Zamli Q Science (General) The performance of meta-heuristic search algorithms highly depends on their intensification and diversification abilities. Different algorithms adopt intensification and diversification strategies in order to obtain better results. Elitism and mutation are common operators that are used for increasing the diversity of the population. Flower Pollination Algorithm (FPA) is one of the recent meta-heuristic algorithms for global optimization. Although proven to be efficient, FPA is prone to get stuck into a local optimum due to the weakness of its population’s diversity especially for multimodal optimization problem. In this paper, first, we propose two strategies based on mutation-FPA (mFPA) and elitism-FPA (eFPA) for t-way test generation (t refer to interaction strength). Then, a comparison between mFPA and eFPA is studied to analysis the effect of introducing elitism and mutation operators on FPA’s performance. The results of the experiments show that both of eFPA and mFPA strategies appear to produce better results than original FPA strategy, however, eFPA performs much better than mFPA in term of tests size. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19740/1/32.%20Comparative%20Study%20of%20Adaptive%20Elitism%20and%20Mutation%20Operators%20in%20Flower%20Pollination%20Algorithm%20for%20Combinatorial%20Testing1.pdf Abdullah, Nasser and Kamal Z., Zamli (2018) Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem. Advanced Science Letters, 24 (10). pp. 7470-7475. ISSN 1936-6612. (Published) https://doi.org/10.1166/asl.2018.12961 DOI: 10.1166/asl.2018.12961 |
| spellingShingle | Q Science (General) Abdullah, Nasser Kamal Z., Zamli Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem |
| title | Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem |
| title_full | Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem |
| title_fullStr | Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem |
| title_full_unstemmed | Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem |
| title_short | Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem |
| title_sort | comparative study of adaptive elitism and mutation operators in flower pollination algorithm for combinatorial testing problem |
| topic | Q Science (General) |
| url | http://umpir.ump.edu.my/id/eprint/19740/ http://umpir.ump.edu.my/id/eprint/19740/ http://umpir.ump.edu.my/id/eprint/19740/ http://umpir.ump.edu.my/id/eprint/19740/1/32.%20Comparative%20Study%20of%20Adaptive%20Elitism%20and%20Mutation%20Operators%20in%20Flower%20Pollination%20Algorithm%20for%20Combinatorial%20Testing1.pdf |