For improving the convergence and distribution together with less computation cost of multi-objective
optimization algorithm, a multi-objective quantum-behaved particle swarm optimization based on QPSO and crowding
distance sorting(MOQPSO-CD) algorithm is proposed. MOQPSO-CD makes full use of QPSO to approximate the true
Pareto optimal solutions quickly, and Gaussian mutation operator is introduced to enhance the diversity of solution.
MOQPSO-CD updates and maintains the archived optimal solutions based on crowding distance sorting technique, whose
purpose is making the leader particles with global optimal ability guide the particle swarm finding the true Pareto optimal
solutions finally. Simulation results show that MOQPSO-CD has better convergence and distribution.