A distance-based PSO approach to solve fuzzy MOPSM with distinct risk measurements

In this study, we propose an improved fuzzy multi-objective portfolio se-lection model (VaR-MOPSM) with distinct risk measurements. The VaR-MOPSM can precisely evaluate the investment and increase the probability of obtaining the expected return. When building the model, fuzzy Value-at-Risk (VaR), which can directly reflect the greatest loss of a selection case under a given confidence level, is used to measure the exact future risk in term of loss. Conversely, variance is utilized to make the selection more stable. In this case, the proposed VaR-MOPSM can provide investors with more significant information for decision-making. To solve this model, we designed a distance based particle swarm optimization algorithm. Finally, the proposed model and algorithm are exemplified by some numerical examples. The experimental results show that the model and algorithm are effective in solving the fuzzy VaR-MOPSM.