Adopting Jaya Algorithm for Team Formation Problem
This paper presents a simple and mighty metaheuristic algorithm, Jaya, which is applied to solve the team formation (TF) problem and it is a very fundamental problem in many databases and expert collaboration networks or web applications. The Jaya does not need any distinctive parameters that requir...
Saved in:
Main Authors: | , |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
Association for Computing MachineryNew York
2020
|
Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/28569/1/Adopting%20Jaya%20Algorithm%20for%20Team%20Formation%20Problem.pdf http://umpir.ump.edu.my/id/eprint/28569/ https://doi.org/10.1145/3384544.3384593 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Malaysia Pahang |
Language: | English |
id |
my.ump.umpir.28569 |
---|---|
record_format |
eprints |
spelling |
my.ump.umpir.285692020-06-26T01:23:05Z http://umpir.ump.edu.my/id/eprint/28569/ Adopting Jaya Algorithm for Team Formation Problem Kader, Md. Abdul Kamal Z., Zamli QA75 Electronic computers. Computer science This paper presents a simple and mighty metaheuristic algorithm, Jaya, which is applied to solve the team formation (TF) problem and it is a very fundamental problem in many databases and expert collaboration networks or web applications. The Jaya does not need any distinctive parameters that require comprehensive tuning, which is usually troublesome and inefficient. Among several optimization methods, Jaya is chosen for TFP because of its simplicity and it always avoids the worst solutions and moving towards the global best solution. This victorious nature makes Jaya Algorithm more powerful and significant as compared to any other contemporary optimization algorithms. To evaluate the efficiency of the Jaya Algorithm (JA) against another metaheuristic algorithm, Sine-Cosine Algorithm (SCA), both algorithms are tested and assessed for the TF problem solution using an ACM dataset containing experts and their skills. The experimental results validate the improved performance of the optimization solutions and the potential of JA with fast convergence for solving TF problems which are better than SCA. Association for Computing MachineryNew York 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28569/1/Adopting%20Jaya%20Algorithm%20for%20Team%20Formation%20Problem.pdf Kader, Md. Abdul and Kamal Z., Zamli (2020) Adopting Jaya Algorithm for Team Formation Problem. In: ICSCA 2020: Proceedings of the 2020 9th International Conference on Software and Computer Applications, 18-21 February 2020 , Langkawi, Malaysia. pp. 62-66.. ISBN 978-1-4503-7665-5 https://doi.org/10.1145/3384544.3384593 |
institution |
Universiti Malaysia Pahang |
building |
UMP Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaysia Pahang |
content_source |
UMP Institutional Repository |
url_provider |
http://umpir.ump.edu.my/ |
language |
English |
topic |
QA75 Electronic computers. Computer science |
spellingShingle |
QA75 Electronic computers. Computer science Kader, Md. Abdul Kamal Z., Zamli Adopting Jaya Algorithm for Team Formation Problem |
description |
This paper presents a simple and mighty metaheuristic algorithm, Jaya, which is applied to solve the team formation (TF) problem and it is a very fundamental problem in many databases and expert collaboration networks or web applications. The Jaya does not need any distinctive parameters that require comprehensive tuning, which is usually troublesome and inefficient. Among several optimization methods, Jaya is chosen for TFP because of its simplicity and it always avoids the worst solutions and moving towards the global best solution. This victorious nature makes Jaya Algorithm more powerful and significant as compared to any other contemporary optimization algorithms. To evaluate the efficiency of the Jaya Algorithm (JA) against another metaheuristic algorithm, Sine-Cosine Algorithm (SCA), both algorithms are tested and assessed for the TF problem solution using an ACM dataset containing experts and their skills. The experimental results validate the improved performance of the optimization solutions and the potential of JA with fast convergence for solving TF problems which are better than SCA. |
format |
Conference or Workshop Item |
author |
Kader, Md. Abdul Kamal Z., Zamli |
author_facet |
Kader, Md. Abdul Kamal Z., Zamli |
author_sort |
Kader, Md. Abdul |
title |
Adopting Jaya Algorithm for Team Formation Problem |
title_short |
Adopting Jaya Algorithm for Team Formation Problem |
title_full |
Adopting Jaya Algorithm for Team Formation Problem |
title_fullStr |
Adopting Jaya Algorithm for Team Formation Problem |
title_full_unstemmed |
Adopting Jaya Algorithm for Team Formation Problem |
title_sort |
adopting jaya algorithm for team formation problem |
publisher |
Association for Computing MachineryNew York |
publishDate |
2020 |
url |
http://umpir.ump.edu.my/id/eprint/28569/1/Adopting%20Jaya%20Algorithm%20for%20Team%20Formation%20Problem.pdf http://umpir.ump.edu.my/id/eprint/28569/ https://doi.org/10.1145/3384544.3384593 |
_version_ |
1672610905624412160 |