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.
Highlights
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
We propose a new meta-heuristic algorithm which is called Jaya algorithm by using a single-point crossover and invoking a modified swap operator to accelerate the search of it
In IJMSO, we present a modified swap operator MSO(a,b,c), where a is the skillid and b and c are the indices of experts that have the skill from experts’ list
Summary
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. The authors in Huang et al (2017) considered a team formation based on the work time availability and skills for each expert in order to form an effective team. Particle swarm optimization (PSO) and genetic algorithm (GA) have been applied in a small number of research to solve (TFP) (Haupt and Haupt 2004). The authors in Han et al (2017) combine the communication cost and geographical proximity into a unified objective function to solve TFP They applied their algorithm to optimize the proposed objective function using a genetic algorithm. Due to a large number of experts in a social network, the formation of feasible teams with various skill set requires an efficient optimization algorithm. We conclude and the future work made up Sect. 6
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
