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An Improved African Buffalo Optimization Algorithm for Collaborative Team Formation in Social Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Elashmawi, Walaa Hassan |
| Copyright Year | 2018 |
| Abstract | Collaborative team formation in a social network is an important aspect for solving a real-world problem that requires different expert skills to achieve it. In this paper, we propose an improved African Buffalo Optimization algorithm integrated with discrete crossover operator conjointly with swap sequence for efficient team formation whose members can assist in solving a given problem with minimum communication cost. The proposed algorithm is called Improved African Buffalo Optimization algorithm (IABO). In IABO, a new concept of swap sequence applied to improve the performance by generating better team members that cover all the required skills. To the best of our knowledge, this is the first work that considers the African Buffalo Optimization algorithm for collaborative team formation in a social network of experts. A set of experiments have been done on two popular real-world benchmark datasets (i.e., DBLP and Stack Overflow) to determine the efficiency of the proposed algorithm in team formation. The results demonstrate the effectiveness of the IABO algorithm in comparison with GA, PSO and standard African Buffalo Optimization algorithm (ABO). |
| Starting Page | 16 |
| Ending Page | 29 |
| Page Count | 14 |
| File Format | PDF HTM / HTML |
| DOI | 10.5815/ijitcs.2018.05.02 |
| Volume Number | 10 |
| Alternate Webpage(s) | http://www.mecs-press.org/ijitcs/ijitcs-v10-n5/IJITCS-V10-N5-2.pdf |
| Alternate Webpage(s) | https://doi.org/10.5815/ijitcs.2018.05.02 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
