Improvement of simplified particle swarm optimization in big data environment

Aiming at the complex optimization problems such as high dimension, strong constraints and multi-objective in big data, an improved swarm intelligence optimization algorithm, Lions Simplified Particle Swarm Optimization(LSA-SPSO), is proposed in this paper. The algorithm integrates the grouping idea of Lions algorithm into the simplified particle swarm optimization algorithm, and divides the particles into three groups for optimization. Each group uses different learning factors and dimension vectors to help the population perform different search mechanisms, thus enhancing the diversity of the population. In addition, introducing population breeding can help the particles to jump out of the local optimum position and improve the global search performance of the algorithm. The simulation results show that the improved algorithm proposed in this paper effectively improves the shortcomings of traditional swarm intelligence algorithm, and can be better applied to big data.