Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation Problem

Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.

On Participation Constrained Team Formation

The task assignment on the Internet has been widely applied to many areas, e.g., online labor market, online paper review and social activity organization. In this paper, we are concerned with the task assignment problem related to the online labor market, termed as CLUSTERHIRE. We improve the definition of the CLUSTERHIRE problem, and propose an efficient and effective algorithm, entitled INFLUENCE. In addition, we place a participation constraint on CLUSTERHIRE. It constrains the load of each expert in order to keep all members from overworking. For the participation-constrained CLUSTERHIRE problem, we devise two algorithms, named PROJECTFIRST and ERA. The former generates a participation- constrained team by adding experts to an initial team, and the latter generates a participation-constrained team by removing the experts with the minimum influence from the universe of experts. The experimental evaluations indicate that 1） INFLU- ENCE performs better than the state-of-the-art algorithms in terms of effectiveness and time efficiency; 2） PROJECTFIRST performs better than ERA in terms of time efficiency, yet ERA performs better than PROJECTFIRST in terms of effectiveness.

An Effective Framework for Fast Expert Mining in Collaboration Networks:A Group-Oriented and Cost-Based Method

The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before. Given a task and a graph of experts where each expert possesses some skills, we tend to find an effective team of experts who axe able to accomplish the task. This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is, considering each expert has the minimum required level of skill. Here, we generalize the problem in multiple perspectives. First, a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors. Second, the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them. By considering the groups, search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members. Third, the existing RarestFirst algorithm is extended to more generalized version, and finally the cost definition is customized to improve the efficiency of selected team. Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness, our proposed framework is achieved well in practice.