  Choosing a committee with independent members in social networks can be named
as a problem in group selection and independence in the committee is considered
as the main criterion of this selection. Independence is calculated based on
the social distance between group members. Although there are many solutions to
solve the problem of group selection in social networks, such as selection of
the target set or community detection, just one solution has been proposed to
choose committee members based on their independence as a measure of group
performance. In this paper, a new adaptive hybrid algorithm is proposed to
select the best committee members to maximize the independence of the
committees. This algorithm is a combination of particle swarm optimization
(PSO) algorithm with two local search algorithms. The goal of this work is to
combine exploration and exploitation to improve the efficiency of the proposed
algorithm and obtain the optimal solution. Additionally, to combine local
search algorithms with particle swarm optimization, an effective selection
mechanism is used to select a suitable local search algorithm to combine with
particle swarm optimization during the search process. The results of
experimental simulation are compared with the well-known and successful
metaheuristic algorithms. This comparison shows that the proposed method
improves the group independence by at least 21%.
