An improved multi-objective cultural algorithm based on particle swarm optimization(PSO-IMOCA) is proposed
to solve multi-objective optimization problem. Population space evolves with the improved multi-objective particle swarm
optimization strategy. Three kinds of knowledge, situational knowledge, normative knowledge and history knowledge, are
redefined to accordance with the solution of multi-objective problem in belief space. The interaction between belief space and
population space is implemented by the adaptive accept function and influence function. Simulation results of the benchmark
test functions show that the improved multi-objective cultural algorithm can possess good uniformity and convergence as well
as maintain the diversity of Pareto optimal solution.