A multi-population evolutionary algorithm for multi-objective constrained portfolio optimization problem

Due to the rapid development of the financial market, the portfolio selection problem has become of the most complex problem in finance. This paper proposes a new multi-objective evolutionary algorithm based on multi-population, called MP-MOEA, to handle the multi-objective constrained portfolio opt...

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Veröffentlicht in:The Artificial intelligence review Jg. 56; H. Suppl 3; S. 3299 - 3340
Hauptverfasser: Hemici, Meriem, Zouache, Djaafar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.12.2023
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Archives & records
Artificial Intelligence
Complexity
Computer Science
Convergence
Crowding
Datasets
Economic development
Evolutionary algorithms
Experiments
Financial market
Financial markets
Functional heads
Genetic algorithms
Global local relationship
International trade
Investment analysis
Multiple objective analysis
Objectives
Optimization
Political aspects
Portfolios
Quantitative analysis
Statistical analysis
Variance analysis
Multi-objective constrained portfolio optimization problem (MOCPOP)
Multiobjective evolutionary algorithm (MOEA)
Multi-population
ISSN:0269-2821, 1573-7462
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Zusammenfassung:Due to the rapid development of the financial market, the portfolio selection problem has become of the most complex problem in finance. This paper proposes a new multi-objective evolutionary algorithm based on multi-population, called MP-MOEA, to handle the multi-objective constrained portfolio optimization problem (MOCPOP) in order to achieve an optimal trade-off between return and risk. MP-MOEA uses a multi-population strategy to improve the solution’s quality and considerably accelerate the convergence. Furthermore, two types of archives (local and global) are employed, where the archives local are used to store the non-dominated solutions corresponding to each subpopulation, and the external archive global is used to store the Pareto solutions. The external archive global is controlled using crowding distance to limit the archive size and avoid increasing the complexity of the MP-MOEA algorithm. Several experiments are conducted on two datasets of instances to compare our algorithm with three elevant state-of-art algorithms including AR-MOEA, MOEA/D-AGR, MOEA/D-GR, MOEA/D, MODEwAwL, and MOPSO. The first dataset consists of 5 instances from OR-Library, while other dataset consists 15 instances from NGINX. Statistical analysis of the comparative results obtained using ANOVA and Wilcoxon test demonstrate the merits and the outperformance of our MP-MOEA algorithm.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10604-2