# 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Statement of Problem and Notation

- (i)
- ${L}_{m}>0$ for $m=1,\dots ,M$, This limitation ensures that at least one asset from each class should be chosen;
- (ii)
- $k\ge M$, which means the portfolio should have more assets that classes (this note is concluded from the first point).

## 4. Methodologies

#### 4.1. NSGA II Algorithm

#### 4.2. The Proposed 2-Phase NSGA II Algorithm

**Pr1**:

**Pr1**could be placed in box ${S}_{1}$ or ${S}_{2}$. If it is placed in box ${S}_{1}$, node k locates on optimal front too; otherwise, there is a new solution improving last position and optimal front layer (Figure 1). This procedure is continued till all peer nodes are examined. It needs to be noted that we may not find a practical solution for pair nodes in stopping condition. In this situation, the algorithm is kept fixed for the first solution of pair nodes and is switched to the node which is next to pair. Finally, Pareto-optimal front is updated and the out coming real Pareto optimal set is archived.

**Pr1**is so that the improvement is possible in direction of first objective function. All above procedure for problem

**Pr1**is repeated for this model. Thus, problem

**Pr2**can be written as follows:

**Pr2**:

#### 4.3. The Algorithms Procedure

- (1)
- Set $t=0$ and produce the random population ${P}_{0}$ of size N.
- (2)
- Assess the objective functions and sort the solutions based on dominance and non-dominance relation operator.
- (3)
- Select the solutions as parents based on uniform selection.
- (4)
- Affect the selected solutions by crossover and mutation operators to produce offspring population ${Q}_{0}$ of size N.
- (5)
- Evaluate the new offspring based on objective functions.
- (6)
- ${R}_{t}={P}_{0}\cup {\Phi}_{0}$ and sort the ${R}_{t}$ solution based on dominance and non-dominance relation (swift non-dominated kind) and make fronts (${F}_{1}$, ${F}_{2}$, …).
- (7)
- ${P}_{t+1}=\varnothing $ and $i=1$.
- (8)
- Until $\left|{P}_{t+1}\right|+\left|{F}_{i}\right|\le N$, ${P}_{t+1}={P}_{t+1}\cup {F}_{i}$, $i=i+1$.
- (9)
- If $\left|{P}_{t+1}\right|+\left|{F}_{i}\right|\ge N$, calculate crowding distance for all solutions in ${F}_{i}$.
- (10)
- Sort (${F}_{i}$, $<n$)
- (11)
- ${P}_{t+1}={P}_{t+1}\cup {F}_{i}$ $[1:(N-\left|{P}_{t+1}\right|)]$
- (12)
**Stopping criteria**: End the algorithm if the stopping criterion is met; otherwise, go to step 2.

- (1)
- After normalizing array A, the weight of one or more classes may not be within the lower and upper limits. For overbearing this problem, the weight of that class or those classes should be calculated again using Equation (11). This process is continued until the weight of all classes lies within their lower and upper limits.
- (2)
- The weight of each class should equal the sum of the weights of the assets existed in that class. When there is only one asset in a given class, it is clear that the weight of this asset is unlikely to be equal to the weight of the class that it belongs to. Therefore, we consider the weight of the class for the asset and investigate whether this new weight satisfies quantity constraint. If this provision is not confirmed, the weight is calculated for the second time. If the weight violates the upper limit, the new weight is replaced with upper limit and the other weights are normalized again. This process continues until all weights are located in the lower and upper bounds span.
- (3)
- Given the fact that selecting at least one asset from each class is essential in the model (${L}_{m}>0$), we are assured of choosing one asset from each class at the same time as filling the cells of B chromosome.

#### 4.4. Genetic Operators

## 5. Performance Evaluation

#### 5.1. Scenario Description

#### 5.1.1. Data Sets

#### 5.1.2. Comparison Metrics

- (1)
- (2)
- NPS: number of solutions in the Pareto-front;
- (3)
- MS: the maximum spread that is the Euclidean distance between boundary solutions;
- (4)
- S: the spacing which is proceeded to calculate the space between two adjacent solutions;
- (5)
- CS: the coverage set which is used to survey the preference of the algorithms.

#### 5.1.3. Parameter Settings

#### 5.2. Experimental Results

#### Analysis of the Results

## 6. Conclusions and Future Developments

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 3.**Pareto-front of non-dominated solutions yielded by both algorithms for five test problems.

**Figure 4.**Pareto-front of non-dominated solutions yielded by both algorithms for five test problems.

Problem Index | Data Source | Number of Assets |
---|---|---|

Port 1 | Hong Kong, hang seng | 31 |

Port 2 | German, Dax 100 | 85 |

Port 3 | British, FTSE 100 | 89 |

Port 4 | US, S&P 100 | 98 |

Port 5 | Japanese, Nikkei 225 | 225 |

Metric | Formula |
---|---|

MID | $\frac{{\sum}_{i=1}^{N}{C}_{i}}{N}$, ${C}_{i}=\sqrt{{({f}_{i}^{1}-{f}_{0}^{1})}^{2}-{({f}_{i}^{2}-{f}_{0}^{2})}^{2}}$ |

MS | $\sqrt{{\sum}_{m=1}^{M}{\sum}_{i=1}^{\left|Q\right|}{\left(max{f}_{m}^{i}-min{f}_{m}^{i}\right)}^{2}}$ |

S | $\sqrt{\frac{1}{\left|Q\right|}{\sum}_{i=1}^{\left|Q\right|}{\left({d}_{i}-\overline{d}\right)}^{2}}$ |

CS | $C(A,B)=\frac{\left|\right\{b\in B|\exists a\in A:a\le b\}|}{\left|B\right|}$ |

Problems | $\mathit{NoG}$ | $\mathit{popsize}$ | k | M |
---|---|---|---|---|

Port 1 | 400 | 100 | 10 | 8 |

Port 2 | 500 | 200 | 20 | 17 |

Port 3 | 300 | 200 | 20 | 18 |

Port 4 | 500 | 200 | 20 | 14 |

Port 5 | 300 | 200 | 30 | 23 |

Problem Size | Algorithm and Index | ||||

MID | NPS | ||||

Port | N | NSGA II | 2-p NSGA II | NSGA II | 2-p NSGA II |

Port 1 | 31 | 0.28343 | 0.27433 | 273 | 6481 |

Port 2 | 85 | 0.07428 | 0.07281 | 1451 | 51,032 |

Port 3 | 89 | 0.13528 | 0.1381 | 1358 | 43,487 |

Port 4 | 98 | 0.10234 | 0.1011 | 678 | 36,348 |

Port 5 | 225 | 0.31602 | 0.31365 | 742 | 49,061 |

MS | S | ||||

Port | N | NSGA II | 2-p NSGA II | NSGA II | 2-p NSGA II |

Port 1 | 31 | 0.12025 | 0.11623 | 0.20062 | 0.01591 |

Port 2 | 85 | 0.06574 | 0.06453 | 0.06887 | 0.00284 |

Port 3 | 89 | 0.1132 | 0.11276 | 0.15946 | 0.0056 |

Port 4 | 98 | 0.19875 | 0.19848 | 0.34357 | 0.00777 |

Port 5 | 225 | 0.31602 | 0.31365 | 0.22209 | 0.00558 |

C | |||||

Port | N | NSGA II | 2-p NSGA II | ||

Port 1 | 31 | 0.00694 | 0.80952 | ||

Port 2 | 85 | 0.01813 | 0.77739 | ||

Port 3 | 89 | 0.03507 | 0.8947 | ||

Port 4 | 98 | 0.01618 | 0.82006 | ||

Port 5 | 225 | 0.01861 | 0.90701 |

Metric | p-Value |
---|---|

MID | 0.852 |

NPS | 0.000 |

MS | 0.027 |

S | 0.000 |

CS | 0.000 |

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## Share and Cite

**MDPI and ACS Style**

Eftekharian, S.E.; Shojafar, M.; Shamshirband, S. 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization. *Algorithms* **2017**, *10*, 130.
https://doi.org/10.3390/a10040130

**AMA Style**

Eftekharian SE, Shojafar M, Shamshirband S. 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization. *Algorithms*. 2017; 10(4):130.
https://doi.org/10.3390/a10040130

**Chicago/Turabian Style**

Eftekharian, Seyedeh Elham, Mohammad Shojafar, and Shahaboddin Shamshirband. 2017. "2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization" *Algorithms* 10, no. 4: 130.
https://doi.org/10.3390/a10040130