Portfolio investment is a complicated combinatorial optimization problem,and is a NP-hard problem,which is difficult to be solved by traditional algorithm.In this paper,a quadratic particle swarm optimization (QPSO) is applied to solving the problem of portfolio investment optimization. In addition,self adapting parameters are applied.The result of the experiment shows that the QPSO can both escape local optimum and speed up global optimal convergence,and outperforms particle swarm optimization in some sense.
|Number of pages||3|
|Journal||Computer Applications and Software|
|Publication status||Published - 30 Jun 2009|
- particle swarm optimization
- portfolio investment
- quadratic particle swarm optimization