Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Mean-semivariance models for fuzzy portfolio selection
Journal of Computational and Applied Mathematics
Multi-objective possibilistic model for portfolio selection with transaction cost
Journal of Computational and Applied Mathematics
Fuzzy multi-objective portfolio selection model with transaction costs
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Two-stage fuzzy stochastic programming with Value-at-Risk criteria
Applied Soft Computing
Fuzzy power system reliability model based on value-at-risk
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Mean-Entropy Models for Fuzzy Portfolio Selection
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
This study proposes an novel fuzzy multi-objective model that can evaluate the invest risk properly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk is used to evaluate the exact future risk, in term of loss. And, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To solve this model, a new Pareto-optimal set based multiobjective particle swarm optimization algorithm is designed to obtain better solutions among the Pareto-front. At the end of this study, the proposed model and algorithm are exemplified by one numerical example. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem.