A Population Based Incremental Learning Algorithm for the Multiobjective Portfolio
- DOI
- 10.2991/mmsta-19.2019.25How to use a DOI?
- Keywords
- portfolio; multiobjective optimization algorithm; evolutionary algorithm
- Abstract
Multiobjective optimization problem Portfolio is a hard–decision–making problem in investment management. With respect to how to obtain the multiobjective candidate decisive solutions for Portfolio, a multiobjective optimization method M–PBIL (Multiobjective Population Based Incremental Learning) to Portfolio is proposed. Different from the traditional evolutionary algorithms which generate individuals based on the recombination of the current ones, M–PBIL follows the strategy of PBIL to generate individuals based on probability model and researches three key technologies in solving multiobjective optimization problems. First, for the multiobjective optimization problem in continuous space, a real number–based coding scheme is proposed, which can overcome the defects of binary coding such as code redundancy and probability conflict. In the second place, a variable probability model for the gene bit is designed so as to realize the dynamic partition for the intervals of decision variables. Next, a dominance and representativeness–based assessment mechanic is employed for the selection of non–dominated solutions of multiobjective optimization problem. The performances of the M–PBIL are evaluated by convergence and distribution and compared with the representative NSGAII on benchmark data. The experimental results show that M–PBIL outperforms NSGAII in convergence and distribution.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Shichen Hu AU - Fang Li AU - Xiaona Deng AU - Yang Liu PY - 2019/12 DA - 2019/12 TI - A Population Based Incremental Learning Algorithm for the Multiobjective Portfolio BT - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019) PB - Atlantis Press SP - 120 EP - 123 SN - 2352-538X UR - https://doi.org/10.2991/mmsta-19.2019.25 DO - 10.2991/mmsta-19.2019.25 ID - Hu2019/12 ER -