A new three-dimensional encoding multiobjective evolutionary algorithm with application to the portfolio optimization problem

The existing evolutionary algorithm techniques have limited capabilities in solving large-scale combinatorial problems due to their large search space, making impractical the examination of big real-world instances. In this paper, we address this issue by introducing a new algorithm that incorporate...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 163; pp. 186 - 203
Main Author: Liagkouras, Konstantinos
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.01.2019
Elsevier Science Ltd
Subjects:
Algorithms
Combinatorial analysis
Computational mathematics
Computational time
Encoding
Evolutionary algorithms
Expert systems
Fractions
Genetic algorithms
Multi-objective optimization
Multiple objective analysis
Optimal allocation
Optimization
Optimization algorithms
Portfolios
Resource allocation
Risk measures
Computational time
Multi-objective optimization
Evolutionary algorithms
Optimal allocation
Risk measures
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:The existing evolutionary algorithm techniques have limited capabilities in solving large-scale combinatorial problems due to their large search space, making impractical the examination of big real-world instances. In this paper, we address this issue by introducing a new algorithm that incorporates a coding structure specially designed to keep the processing time invariant to the size of the examined test instance, allowing the consideration of large-scale problems for a fraction of time required by other techniques. We test the performance of the proposed algorithm to the optimal allocation of limited resources to a number of competing investment opportunities for optimizing the objectives. We believe that the proposed algorithm can be particularly useful in other contexts too, subject to adaptations relevant to specific problem requirements. •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.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.08.025