Multi-Objective Optimization Algorithm Based on Quantum-behaved Particle Swarm and Adaptive Grid

In order to find more true Pareto optimal solutions and improve their uniformity of distribution,a multi-objective quantum particle swarm optimization algorithm based on quantum-behaved particle swarm optimization(QPSO) and adaptive grid(MOQPSO) is proposed.MOQPSO makes full use of the superiority of quantum-behaved particle swarm optimization to approximate the true Pareto optimal solutions quickly,and Gaussian mutation operator is introduced to enhance the diversity of searched solutions.MOQPSO reserves the found Pareto optimal solutions by setting an external memory,and then updates and maintains the optimal solutions based on adaptive grid,in order to guide the particle swarm finding the true Pareto optimal solutions finally by the leader particles from external memory.Simulation results denote that MOQPSO is of better convergence and more uniform distributing performance.