Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Theory and Practice of Uncertain Programming
Theory and Practice of Uncertain Programming
On minimum-risk problems in fuzzy random decision systems
Computers and Operations Research
A fuzzy decision support system for strategic portfolio management
Decision Support Systems
Measurability criteria for fuzzy random vectors
Fuzzy Optimization and Decision Making
Particle swarm optimization-based algorithms for TSP and generalized TSP
Information Processing Letters
Molecular docking with multi-objective Particle Swarm Optimization
Applied Soft Computing
Brief paper: A swarm intelligence approach to the synthesis of two-dimensional IIR filters
Engineering Applications of Artificial Intelligence
A new particle swarm optimization for the open shop scheduling problem
Computers and Operations Research
The modes of convergence in the approximation of fuzzy random optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Uncertainty Analysis and Decision Making; Guest Editors: Yan-Kui Liu, Baoding Liu, Jinwu Gao
Application of Monte Carlo AHP in ranking dental quality attributes
Expert Systems with Applications: An International Journal
Kernel-based Monte Carlo simulation for American option pricing
Expert Systems with Applications: An International Journal
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Particle swarm optimization with crazy particles for nonconvex economic dispatch
Applied Soft Computing
Simplifying Particle Swarm Optimization
Applied Soft Computing
Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability
Expert Systems with Applications: An International Journal
A semi-variance portfolio selection model for military investment assets
Expert Systems with Applications: An International Journal
An optimization model of the portfolio adjusting problem with fuzzy return and a SMO algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy confidence intervals for mean of Gaussian fuzzy random variables
Expert Systems with Applications: An International Journal
An intelligent forecasting model based on robust wavelet ν-support vector machine
Expert Systems with Applications: An International Journal
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Algorithmic determination of the maximum possible earnings for investment strategies
Decision Support Systems
An improved hybrid particle swarm optimization algorithm for fuzzy p-hub center problem
Computers and Industrial Engineering
A fuzzy supply chain contract problem with pricing and warranty
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The Markowitz's mean-variance (M-V) model has received widespread acceptance as a practical tool for portfolio optimization, and his seminal work has been widely extended in the literature. The aim of this article is to extend the M-V method in hybrid decision systems. We suggest a new Chance-Variance (C-V) criterion to model the returns characterized by fuzzy random variables. For this purpose, we develop two types of C-V models for portfolio selection problems in hybrid uncertain decision systems. Type I C-V model is to minimize the variance of total expected return rate subject to chance constraint; while type II C-V model is to maximize the chance of achieving a prescribed return level subject to variance constraint. Hence the two types of C-V models reflect investors' different attitudes toward risk. The issues about the computation of variance and chance distribution are considered. For general fuzzy random returns, we suggest an approximation method of computing variance and chance distribution so that C-V models can be turned into their approximating models. When the returns are characterized by trapezoidal fuzzy random variables, we employ the variance and chance distribution formulas to turn C-V models into their equivalent stochastic programming problems. Since the equivalent stochastic programming problems include a number of probability distribution functions in their objective and constraint functions, conventional solution methods cannot be used to solve them directly. In this paper, we design a heuristic algorithm to solve them. The developed algorithm combines Monte Carlo (MC) method and particle swarm optimization (PSO) algorithm, in which MC method is used to compute probability distribution functions, and PSO algorithm is used to solve stochastic programming problems. Finally, we present one portfolio selection problem to demonstrate the developed modeling ideas and the effectiveness of the designed algorithm. We also compare the proposed C-V method with M-V one for our portfolio selection problem via numerical experiments.