Phase angle particle swarm optimization(PAPSO) algorithm is proposed to solve multi-objective optimization
problems. The algorithm adopts phase angle reflection, such that particles can rapidly search solutions in phase angle space,
which is only dependent on normalized multi-objective functions. Sharing pool concept is introduced into the particle
flight information sharing mechanism, and the sharing pool update strategy based on the related predominance sorting
and similarity degree sorting is presented to improve the diversity of Pareto solutions. Sigma leading strategy and chaos
variation operation balance its ability of finding optimal solutions rapidly and extensively. Cases related to benchmark multi-
objective test functions and wide area damping control multi-objective optimization in power systems verify the feasibility
and effectiveness of the proposed algorithm.