Born and death is the nature of lives, but most swarm in- telligence algorithm did not reflect this important property. Based on Particle Swarm Optimization, the concept of life span is introduced to control the activity generation of particles. Furthermore, the differen- tial operator is applied to enhance the convergence and precision. The performance of propose algorithm, along with PSO and DE, is tested on benchmark functions. Results show that life span and differential op- erator greatly improved PSO and with well-balanced exploration and exploitation characteristic. 

Swarm Intelligence, PSO, Life Span, Differential, Mutation, Optimization, Algorithm.