A new three-dimensional encoding multiobjective evolutionary algorithm with application to the portfolio optimization problem
作者：
Highlights：
• Existing techniques have limited capabilities in solving large combinatorial problems.
• The proposed algorithm keeps the processing time invariant to the problem’s size.
• It is tested to optimal allocation of limited resources to a number of investments.
• It outperforms the other techniques in terms of performance and computational time.
• It can be proved very useful in problems with large number of alternative choices.
摘要
•Existing techniques have limited capabilities in solving large combinatorial problems.•The proposed algorithm keeps the processing time invariant to the problem’s size.•It is tested to optimal allocation of limited resources to a number of investments.•It outperforms the other techniques in terms of performance and computational time.•It can be proved very useful in problems with large number of alternative choices.
论文关键词：Multi-objective optimization,Evolutionary algorithms,Risk measures,Optimal allocation,Computational time
论文评审过程：Received 11 April 2018, Revised 14 August 2018, Accepted 19 August 2018, Available online 5 September 2018, Version of Record 21 November 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.08.025
