Abstract: General particle swarm optimization for solving high-dimensional optimization problems potentially getting trapped in sub-optimal “curse”, the paper designs a pyramid-type cooperative approach to particle swarm optimizations with multi-dimensions algorithm(PCPSO-M), which combines PSO’s earlier convergence with CPSO’s stronger searching optimal ability. The particle swarm is divided into three layers, particles of which interacts internally and externally. For excessive dimensions on the top, half of the "good" fitness of the particles replace the other half of "poor" in the fitness particles. So the "learning" way of coordination is a very effective solution to the problem of high dimension, which makes up for deficiencies of CPSO algorithm; especially in the Rosenbrock, Quadric function tests, a satisfactory solution can be got.

Key words: CPSO, PSO, cooperative learning, pyramid