Research on the Effect of DPSO in Team Selection Optimization under the Background of Big Data

Abstract

Team selection optimization is the foundation of enterprise strategy realization; it is of great significance for maximizing the effectiveness of organizational decision-making. Thus, the study of team selection/team foundation has been a hot topic for a long time. With the rapid development of information technology, big data has become one of the significant technical means and played a key role in many researches. It is a frontier of team selection study by the means of combining big data with team selection, which has the great practical significance. Taking strategic equilibrium matching and dynamic gain as association constraints and maximizing revenue as the optimization goal, the Hadoop enterprise information management platform is constructed to discover the external environment, organizational culture, and strategic objectives of the enterprise and to discover the potential of the customer. And in order to promote the renewal of production and cooperation mode, a team selection optimization model based on DPSO is built. The simulation experiment method is used to qualitatively analyze the main parameters of the particle swarm optimization in this paper. By comparing the iterative results of genetic algorithm, ordinary particle swarm algorithm, and discrete particle swarm algorithm, it is found that the DPSO algorithm is effective and preferred in the study of team selection with the background of big data.