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.