Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm

Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm

Although showing competitive performances in many real-world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. Addressing these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learnin...

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Bibliographic Details
Published in:Lecture Notes in Networks and Systems
Main Author: Zamli K.Z.; Din F.; Ramli N.; Ahmed B.S.
Format: Book chapter
Language:English
Published: Springer 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066131390&doi=10.1007%2f978-981-13-6031-2_3&partnerID=40&md5=2bfed0836b6229d2f1e637a337ae2e8a
id 2-s2.0-85066131390
spelling 2-s2.0-85066131390
Zamli K.Z.; Din F.; Ramli N.; Ahmed B.S.
Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
2019
Lecture Notes in Networks and Systems
67

10.1007/978-981-13-6031-2_3
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066131390&doi=10.1007%2f978-981-13-6031-2_3&partnerID=40&md5=2bfed0836b6229d2f1e637a337ae2e8a
Although showing competitive performances in many real-world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. Addressing these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) has been developed in the literature. This paper describes the adoption of Fuzzy Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) for software module clustering problem. Comparative studies with the original Teaching Learning based Optimization (TLBO) and other Fuzzy TLBO variant demonstrate that ATLBO gives superior performance owing to its adaptive selection of search operators based on the need of the current search. © Springer Nature Singapore Pte Ltd. 2019.
Springer
23673370
English
Book chapter
author Zamli K.Z.; Din F.; Ramli N.; Ahmed B.S.
spellingShingle Zamli K.Z.; Din F.; Ramli N.; Ahmed B.S.
Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
author_facet Zamli K.Z.; Din F.; Ramli N.; Ahmed B.S.
author_sort Zamli K.Z.; Din F.; Ramli N.; Ahmed B.S.
title Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
title_short Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
title_full Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
title_fullStr Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
title_full_unstemmed Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
title_sort Software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm
publishDate 2019
container_title Lecture Notes in Networks and Systems
container_volume 67
container_issue
doi_str_mv 10.1007/978-981-13-6031-2_3
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066131390&doi=10.1007%2f978-981-13-6031-2_3&partnerID=40&md5=2bfed0836b6229d2f1e637a337ae2e8a
description Although showing competitive performances in many real-world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. Addressing these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) has been developed in the literature. This paper describes the adoption of Fuzzy Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) for software module clustering problem. Comparative studies with the original Teaching Learning based Optimization (TLBO) and other Fuzzy TLBO variant demonstrate that ATLBO gives superior performance owing to its adaptive selection of search operators based on the need of the current search. © Springer Nature Singapore Pte Ltd. 2019.
publisher Springer
issn 23673370
language English
format Book chapter
accesstype
record_format scopus
collection Scopus
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