An improved particle swarm optimization with a new swap operator for team formation problem
Abstract Formation of effective teams of experts has played a crucial role in successful projects especially in social networks. In this paper, a new particle swarm optimization (PSO) algorithm is proposed for solving a team formation optimization problem by minimizing the communication cost among e...
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Islamic Azad University
2018-07-01
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Online Access: | http://link.springer.com/article/10.1007/s40092-018-0282-6 |
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doaj-7cd10aba3035453889eb0e83c61693442021-03-02T11:12:52ZengIslamic Azad UniversityJournal of Industrial Engineering International1735-57022251-712X2018-07-01151537110.1007/s40092-018-0282-6An improved particle swarm optimization with a new swap operator for team formation problemWalaa H. El-Ashmawi0Ahmed F. Ali1Mohamed A. Tawhid2Department of Computer Science, Faculty of Computers and Informatics, Suez Canal UniversityDepartment of Computer Science, Faculty of Computers and Informatics, Suez Canal UniversityDepartment of Mathematics and Statistics, Faculty of Science, Thompson Rivers UniversityAbstract Formation of effective teams of experts has played a crucial role in successful projects especially in social networks. In this paper, a new particle swarm optimization (PSO) algorithm is proposed for solving a team formation optimization problem by minimizing the communication cost among experts. The proposed algorithm is called by improved particle optimization with new swap operator (IPSONSO). In IPSONSO, a new swap operator is applied within particle swarm optimization to ensure the consistency of the capabilities and the skills to perform the required project. Also, the proposed algorithm is investigated by applying it on ten different experiments with different numbers of experts and skills; then, IPSONSO is applied on DBLP dataset, which is an example for benchmark real-life database. Moreover, the proposed algorithm is compared with the standard PSO to verify its efficiency and the effectiveness and practicality of the proposed algorithm are shown in our results.http://link.springer.com/article/10.1007/s40092-018-0282-6Particle swarm optimizationTeam formation problemSocial networksSingle-point crossoverSwap operator |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Walaa H. El-Ashmawi Ahmed F. Ali Mohamed A. Tawhid |
spellingShingle |
Walaa H. El-Ashmawi Ahmed F. Ali Mohamed A. Tawhid An improved particle swarm optimization with a new swap operator for team formation problem Journal of Industrial Engineering International Particle swarm optimization Team formation problem Social networks Single-point crossover Swap operator |
author_facet |
Walaa H. El-Ashmawi Ahmed F. Ali Mohamed A. Tawhid |
author_sort |
Walaa H. El-Ashmawi |
title |
An improved particle swarm optimization with a new swap operator for team formation problem |
title_short |
An improved particle swarm optimization with a new swap operator for team formation problem |
title_full |
An improved particle swarm optimization with a new swap operator for team formation problem |
title_fullStr |
An improved particle swarm optimization with a new swap operator for team formation problem |
title_full_unstemmed |
An improved particle swarm optimization with a new swap operator for team formation problem |
title_sort |
improved particle swarm optimization with a new swap operator for team formation problem |
publisher |
Islamic Azad University |
series |
Journal of Industrial Engineering International |
issn |
1735-5702 2251-712X |
publishDate |
2018-07-01 |
description |
Abstract Formation of effective teams of experts has played a crucial role in successful projects especially in social networks. In this paper, a new particle swarm optimization (PSO) algorithm is proposed for solving a team formation optimization problem by minimizing the communication cost among experts. The proposed algorithm is called by improved particle optimization with new swap operator (IPSONSO). In IPSONSO, a new swap operator is applied within particle swarm optimization to ensure the consistency of the capabilities and the skills to perform the required project. Also, the proposed algorithm is investigated by applying it on ten different experiments with different numbers of experts and skills; then, IPSONSO is applied on DBLP dataset, which is an example for benchmark real-life database. Moreover, the proposed algorithm is compared with the standard PSO to verify its efficiency and the effectiveness and practicality of the proposed algorithm are shown in our results. |
topic |
Particle swarm optimization Team formation problem Social networks Single-point crossover Swap operator |
url |
http://link.springer.com/article/10.1007/s40092-018-0282-6 |
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