Multiobjective evolutionary algorithms for complex portfolio optimization problems

2009 ◽  
Vol 8 (3) ◽  
pp. 259-279 ◽  
Author(s):  
Konstantinos P. Anagnostopoulos ◽  
Georgios Mamanis
Keyword(s):  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problems ◽  
Multiobjective Evolutionary Algorithms

scholarly journals Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
B. Y. Qu ◽  
Q. Zhou ◽  
J. M. Xiao ◽  
J. J. Liang ◽  
P. N. Suganthan
Keyword(s):  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Asset Allocation ◽  
Large Scale ◽  
Optimization Problems ◽  
Multiobjective Evolutionary Algorithms ◽  
Pairwise Correlation ◽  
Individual Asset ◽  
Simulation Time ◽  
Selection Of

Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D) algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics

2015 ◽  
Vol 14 (03) ◽  
pp. 535-564 ◽  
Author(s):  
K. Liagkouras ◽  
K. Metaxiotis
Keyword(s):  
Best Practices ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Performance Metrics ◽  
State Of The Art ◽  
The State ◽  
Multiobjective Evolutionary Algorithms ◽  
Wide Range ◽  
Portfolio Optimization Problem

This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Based on the examination of the state-of-the art we provide the best practices for dealing with the complexities of the constrained portfolio optimization problem (CPOP). In particular, rigorous algorithmic and technical treatment is provided for the efficient incorporation of a wide range of real-world constraints into the MOEAs. Moreover, we address special configuration issues related to the application of MOEAs for solving the CPOP. Finally, by examining the state-of-the-art we identify the most appropriate performance metrics for the evaluation of the relevant results from the implementation of the MOEAs to the solution of the CPOP.

Comprehensive Survey of the Hybrid Evolutionary Algorithms

2013 ◽  
Vol 4 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Wali Khan Mashwani
Keyword(s):  
Evolutionary Algorithms ◽  
Pareto Front ◽  
Optimization Problems ◽  
Mathematical Formulation ◽  
Multiobjective Evolutionary Algorithms ◽  
Nsga Ii ◽  
Pareto Optimal Set ◽  
Multiobjective Optimization Problems ◽  
Comprehensive Survey ◽  
Optimal Set

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) is two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization problems (MOPs. The mathematical formulation of a MOP and some basic definitions for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs. The authors present literature review in subsections. Subsection 2.1 provides memetic multiobjective evolutionary algorithms. Subsection 2.2 presents the hybrid versions of well-known Pareto dominance based MOEAs. Subsection 2.4 summarizes some enhanced Versions of MOEA/D paradigm. Subsection 2.5 reviews some multimethod search approaches dealing optimization problems.

scholarly journals An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Fuqing Zhao ◽  
Wenchang Lei ◽  
Weimin Ma ◽  
Yang Liu ◽  
Chuck Zhang
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Search Space ◽  
Multiobjective Evolutionary Algorithms ◽  
Local Optima ◽  
Adaptive Selection ◽  
Convergence Performance ◽  
Evolutionary Operators ◽  
Polynomial Mutation

A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA) is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.

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