Providing an Evolutionary Approach for Multi-objective Portfolio Optimization Problem by Using Evolutionary Algorithm Multi-objective NSGAⅡ

2017 ◽  
Vol 7 (2) ◽  
pp. 70-87
Author(s):  
Maryam Khajouri ◽  
Somaye Khajouri ◽  
Mohammad Saraei
Keyword(s):  
Evolutionary Algorithm ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Evolutionary Approach ◽  
Multi Objective ◽  
Portfolio Optimization Problem

scholarly journals Modeling and Optimizing the Multi-Objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  
Keyword(s):  
Steady State ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Density Estimator ◽  
Nsga Ii ◽  
Spatial Spread ◽  
Multi Objective ◽  
Significant Difference ◽  
Portfolio Optimization Problem

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

Multi-objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
pp. 281-293
Author(s):  
Claudia Guadalupe Gómez-Santillán ◽  
Alejandro Estrada Padilla ◽  
Héctor Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
Nelson Rangel-Valdez ◽  
...  
Keyword(s):  
Portfolio Optimization ◽  
Optimization Problem ◽  
Multi Objective ◽  
Portfolio Optimization Problem ◽  
Fuzzy Parameters

scholarly journals Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management

2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Yabao Hu ◽  
Hanning Chen ◽  
Maowei He ◽  
Liling Sun ◽  
Rui Liu ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Optimization Problem ◽  
Optimality Criteria ◽  
Investment Fund ◽  
Multi Objective ◽  
Portfolio Optimization Problem ◽  
Investment Portfolio Optimization

Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.

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