Abstract: We proposed a multiobjective particle swarm optimization algorithm based on selfadaption partition of searching space, according to the search capability and scale of particles, it was transformed into a multidimensional standard normal distribution with the volume of subsearch space, which finely partitioned search spaces and optimized the distribution of particles in the divided subsearch spaces, and the partition continued during iteration until an optimal solution set was obtained. The experimental results show that the algorithm effectively solves the problem that multiobjective particle swarm optimization algorithm is easy to fall into local extremum, compared with some typical multiobjective particle swarm optimization algorithms, the algorithm has significant advantages in diversity of population and convergence of solution on the performance index of inverted generational distance.

Key words: particle swarm optimization (PSO), multiobjective optimization problem (MOP), multidimensional standard normal distribution, selfadaption partition