Abstract: In order to control the balance of global exploration and local exploitation efficiently,the concept of the Multiple phases coefficient Dynamic control strategy Particle Swarm Optimization（MDPSO） is introduced.In MDPSO algorithm,the search process is divided into three phases in logical,which has specific aim respectively.In each phase,five acceleration coefficients,which represent individual experience,swarm experinece,globe experience,swarm repulsion and globe repulsion,changes with different rules.The major consideration of this modification is to control the balance of attraction and repulsion effectively,which relates the particles and swarm and globe,and to avoid premature convergence in the early process and enhance convergence to the global optimum solution at the end of process.Four well known benchmark functions are used as testing functions for the MDPSO algorithm and the conclusion of testing is presented.