An improved Jaya algorithm with a modified swap operator for solving team formation problem
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METHODOLOGIES AND APPLICATION
An improved Jaya algorithm with a modified swap operator for solving team formation problem Walaa H. El-Ashmawi1,2 · Ahmed F. Ali1 · Adam Slowik3
© The Author(s) 2020
Abstract Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal. Keywords Jaya algorithm · Team formation problem · Social networks · Genetic algorithm · Modified swap operator
1 Introduction Team formation problem (TFP) considers an important role in many real-life applications and in social networks which are extending from software project development to different collaborative tasks. There is a community set of experts associated with a diverse skill sets in social networks. The goal is forming teams that cover the incoming tasks, in which each task requires a set of skills that must be covered by a team Communicated by V. Loia.
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Adam Slowik [email protected] Walaa H. El-Ashmawi [email protected] ; [email protected] Ahmed F. Ali [email protected]
1
Department of Computer Science, Faculty of Computers & Informatics, Suez Canal University, Ismailia, Egypt
2
Faculty of Computer Science, Misr International University, Cairo, Egypt
3
Department of Electronics & Computer Science, Koszalin University of Technology, Koszalin, Poland
members. The problem is how to form a team that should have small communication cost, according to the underlying social network. This problem can be formulated as NP-hard problem (Lappas et al. 2009) that required the development of meta-heuristic algorithms to solve it. Most of the published papers in team formation are using approximation algorithms (Anagnostopoulos et al. 2012; Kargar et al. 2013), which consider diameter and minimum spanning tree as a communication cost (Lappas et al. 2009) or sum of distance from each member to team leader (Kargar and An 2011). The authors in Appel et al. (2014); Li and Shan (2010); Li et al. (201
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