To solve the engineering design problems, by introducing cultural evolution framework, a cultural based multi-
objective particle swarm optimization algorithm with crowding distance sorting is proposed. The redundant particles in the
crowded area are deleted with the distance sorting operator to guarantee the elitism’s uniform distribution. With the distance
value, the global and local best of the particles are selected from the most disperse region in the elitism and situational
knowledge, respectively, so as to enhance its global searching capability. The evolution parameters are adjusted dynamically
according to the changing of distance to improve the convergence speed. Some standard test problems and the comparison
with other algorithms show the effectiveness and robustness of the algorithm.