Abstract: PSO has become one of the effective methods for solving multi-objective optimization problems,and the key of PSO is the proper settings of the inertial,local and global velocity coefficients.To solve the problem,separating settings for each speed coefficient in existing algorithm with ignoring potential relevancies,an improved multi-objective particle optimization for balancing each formula element was proposed.For the purpose of guiding the evolutionary particle swarm in a potential global optimum,our algorithm can dynamically adjust the speed of each particle coefficients to balance inertia,local and global effects of three speed items during the searching process.Thus the searching capability and accuracy of the new algorithm is more accurate.Meanwhile,our algorithm can not only balance the capacity of exploitation and exploration,but also improve the efficiency in solving complex multi-objective optimization problem.The experimental results indicate that the new algorithm outperforms other 5 classical evolutionary algorithms in terms of convergence speed and distribution on 7 multi-objective benchmark functions.

Key words: Particle swarm optimization,Balance,Speed coefficient,Adaptive,Multi-objective optimization