Quantum Multi-objective Evolutionary Algorithm with Particle Swarm Optimization Method

This paper proposes a novel algorithm for Multiobjective Optimization Problems based on Quantum Particle Swarm. To improve performance of original particle swarm optimization algorithm and avoid trapping to local excellent situations, this method constructs the new quantum solutions expression for multi-objective optimization particle swarm. It adopts the non-dominated sorting method for solutions population and use a new population diversity preserving strategy which is based on the Pareto max-min distance. The multi dimensional 0-1 knapsack optimization problems are carried out and the results show that the proposed method can efficiently find Pareto optimal solutions that are closer to Pareto font and better on distribution. Especially, this proposed method is outstanding on more complex high-dimensional optimization problems.

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