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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Main Authors:	Abdullah, Nasser, Kamal Z., Zamli
Format:	Article
Language:	English
Published:	American Scientific Publisher 2018
Subjects:	Q Science (General)
Online Access:	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

id	ump-19740
recordtype	eprints
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 https://doi.org/10.1166/asl.2018.12961 DOI: 10.1166/asl.2018.12961
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General)
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
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.
format	Article
author	Abdullah, Nasser Kamal Z., Zamli
author_facet	Abdullah, Nasser Kamal Z., Zamli
author_sort	Abdullah, Nasser
title	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_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_sort	comparative study of adaptive elitism and mutation operators in flower pollination algorithm for combinatorial testing problem
publisher	American Scientific Publisher
publishDate	2018
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
first_indexed	2023-09-18T22:28:18Z
last_indexed	2023-09-18T22:28:18Z
_version_	1777416114605654016

Comparative Study of Adaptive Elitism and Mutation Operators in Flower Pollination Algorithm for Combinatorial Testing Problem

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