Bat Particle Swarm Optimization Modeling Based on Optimization of Knowledge Transfer Time in Big Data Environment

With the advent of the big data era, the factors affecting knowledge transfer have undergone tremendous changes. Through in-depth analysis of the characteristics of knowledge transfer in the big data environment, the bat particle swarm optimization algorithm is analyzed. In the local search, the algorithm proposed in this paper embeds the particle swarm algorithm to generate candidate optimal bats, and re-competes with the random bats generated by the basic bat algorithm to optimize the population, enriching the diversity of the population. On this basis, fully consider Changes in factors such as the subject of knowledge transfer and the type of knowledge in the big data environment have significantly increased the time spent on knowledge transfer, and the optimal transfer time obtained by the solution has been shortened by 15%