Multi-objective quantum-behaved particle swarm optimization algorithm based on QPSO and crowding distance sorting

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.