关键词: 多目标, 投资组合, 最优化, 量子, 模拟退火

Abstract: Multi-objective portfolio optimization is decides the percentage of the overall portfolio value allocated to each portfolio component with specified risk and return and exchanging expense characteristics to make total investment risk and exchanging expense least,at the same time,make total investment return most and so on.The problem of multi-objective portfolio optimization is a problem of NP_hard.Ordinary methods are hard to be at the holistic best point.In this paper the authors study how to use Quantum-Behaved Partical Swarm Optimization(QPSO)combined Simulated Annealing(SA)algorithm to solve the problem of multiobjective portfolio optimization,and also compare performance of single QPSO with performance of QPSO-SA.Many experiments that optimize the allocation of various stocks in the market of USA using QPSO-SA algorithm and the analyse of experiment result indicate that the QPSO-SA is a kind of efficient and reliable optimization algorithm and it has determinate applied value in the field of multi-objective portfolio optimization.

Key words: multi-objective, portfolio, optimization, quantum, simulated annealing