A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications

Ponsich, Antonin; Jáimes, Antonio López; Coello, Carlos A. Coello

doi:10.1109/tevc.2012.2196800

Cited by 294 publications

(142 citation statements)

References 71 publications

Supporting

Mentioning

128

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, MOEAs can deal with the computational complexity with an increasing number of projects. That is why there are lots of publications devoted to solving portfolio optimization problems using MOEAs and also there are many applications of MOEAs in finance and economics areas [13].…”

Section: Introductionmentioning

confidence: 99%

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

Xiao

Hong

et al. 2018

Complexity

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Xiao

Hong

et al. 2018

Complexity

View full text Add to dashboard Cite

show abstract

“…Multi-objective optimization problems (MOPs) refer to those consisting of multiple contradictory objectives to be optimized simultaneously, which widely exist in real-world applications [1][2][3][4][5]. MOPs with more than three objectives are also called many-objective optimization problems (MaOPs) [6].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Tian

Wang

Zhang

et al. 2017

Complex Intell. Syst.

111

View full text Add to dashboard Cite

Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi-and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of nondominated sorting in multi-and many-objective evolutionary algorithms. The effectiveness of non-dominated sorting is verified by considering two popular evolutionary algorithms, NSGA-II and KnEA, which were designed for solving multiand many-objective optimization problems, respectively. The efficiency of non-dominated sorting is evaluated by comparing several state-of-the-art non-dominated sorting algorithms for multi-and many-objective optimization problems. These results provide important insights to adopt non-dominated sorting in developing novel multi-and many-objective evolutionary algorithms.

show abstract

“…EAs are robust and suitable for effectively obtaining optima, and with a smaller probability than other algorithms of falling into local optima. The methodology of the EAs has been discussed in many literatures [2][3][4][5][6][7][8][9], and EAs are also successfully used in various industrial applications [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A backtracking evolutionary algorithm for power systems

Chiou

Chang

2017

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. This paper presents a backtracking variable scaling hybrid differential evolution, called backtracking VSHDE, for solving the optimal network reconfiguration problems for power loss reduction in distribution systems. The concepts of the backtracking, variable scaling factor, migrating, accelerated, and boundary control mechanism are embedded in the original differential evolution (DE) to form the backtracking VSHDE. The concepts of the backtracking and boundary control mechanism can increase the population diversity. And, according to the convergence property of the population, the scaling factor is adjusted based on the 1/5 success rule of the evolution strategies (ESs). A larger population size must be used in the evolutionary algorithms (EAs) to maintain the population diversity. To overcome this drawback, two operations, acceleration operation and migrating operation, are embedded into the proposed method. The feeder reconfiguration of distribution systems is modelled as an optimization problem which aims at achieving the minimum loss subject to voltage and current constraints. So, the proper system topology that reduces the power loss according to a load pattern is an important issue. Mathematically, the network reconfiguration system is a nonlinear programming problem with integer variables. One three-feeder network reconfiguration system from the literature is researched by the proposed backtracking VSHDE method and simulated annealing (SA). Numerical results show that the perfrmance of the proposed method outperformed the SA method.

show abstract

A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications

Cited by 294 publications

References 71 publications

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

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

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

A backtracking evolutionary algorithm for power systems

Contact Info

Product

Resources

About