Adopting Jaya Algorithm for Team Formation Problem

Kader, Md. Abdul; Zamli, Kamal Z.

doi:10.1145/3384544.3384593

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…The simulation experiments were performed on an Intel Core i5 processor with 8 GB of RAM, using Java Eclipse software and Microsoft Windows 10. The proposed SSR-TF algorithm was compared with the most recent state-of-the-art metaheuristic Hill-Climbing TF, Jaya-TF [ 33 ], and Sine-Cosine-TF [ 32 ] algorithms. The selected performance parameters for team formation are Total Communication-Cost (TC), CPU time in milliseconds, and experts in a team.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Universiti Malaysia Pahang (UMP) dataset (D01) is a medium-size dataset that contains comprehensive information about 96 academicians with 164 skills related to the computer science field. It was collected by Kamal et al [ 32 ] to find successful collaborating teams within the faculty of computing, UMP to run cost-effective projects. This team formation dataset is one of the cleanest available online [ 35 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The Jaya was tested against a state-of-the-art Sine-Cosine algorithm on an ACM dataset containing experts and their skills. The results indicate that Jaya is a reliable team formation algorithm than the Sine-Cosine algorithm [ 32 ]. The same year, Walaa H. El-Ashmawi, minimized the communication cost among skilled individuals in a team with an improved Jaya optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

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A novel state space reduction algorithm for team formation in social networks

et al. 2021

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Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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