Quantum particle swarm optimization method for multi-objective optimization

To improve performance of original particle swarm optimization algorithm and avoid trapping to local excellent situations,a quantum-bit particle swarm optimization(QBPSO) algorithm for multi-objective optimization problems is presented.QBPSO adopts the non-dominated storing method for solutions population and use a new population diversity preserving strategy which is based on the Pareto max-min distance.The multidimensional 0-1knapsack problems are tested and the results show that the proposed method can ef-ficiently find Pareto optimal solutions that are closer to Pareto font and better on distribution.Especially,this proposed method is out-standing on more complex high-dimensional optimization problems.