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Assistant reference point guided evolutionary algorithm for many-objective fuzzy portfolio selection
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DOI：10.1016/j.swevo.2021.100862.png)
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摘要
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Portfolio selection is dedicated to assigning limited capital to available financial assets to achieve an optimal trade-off between profit and risk. This work is targeted at a portfolio selection problem simultaneously consid-ering five optimization objectives namely mean, variance, skewness, kurtosis, and entropy of return. An expert?s acknowledge-based fuzzy number variable, rather than a random variable, is used to model the return of a risk asset, such that a more accurate estimation of the return of a risky asset is achieved. An assistant reference point guided evolutionary algorithm (ARPGEA) is proposed to solve this five-objective portfolio problem. As-sistant reference points are generated for the non-dominated solutions in all dimensions of the objective space. The generated reference points and a comprehensive fitness evaluation method are used to guide the search to-wards promising regions leading to well-distributed and well-converged solutions. ARPGEA is tested on publicly available data sets collected from six market indices including up to 1,203 assets. Experiment results demon-strate the efficiency and effectiveness of ARPGEA in comparison with other five state-of-the-art many-objective evolutionary algorithms.
Keyword:
Fuzzy portfolio selection
Many-objective optimization
Reference point
High-order moment
Entropy
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Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes
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