constrained optimization

In this paper, we propose a modification to the use of the risk aversion coefficient in optimization models, based on research literature and mathematical methods. The modified risk aversion coefficient introduced in this paper can be applied in the maximization part of the model without any adverse effects. By doing so, it can improve the accuracy of meta-heuristic algorithms in finding optimal solutions. To test the efficacy of our proposed model, we applied it to 30 shares of the Tehran Stock Exchange, along with a zero-risk asset, taking into account some limitations in the market. We used a genetic meta-heuristic optimization method to solve the model, and to measure its efficiency, we compared the results of the optimization process with 2500 randomly generated portfolios that were within the problem's constraints. Our results show that our model outperforms the random portfolios in terms of both risk factors and return. In conclusion, our proposed modification to the risk aversion coefficient can improve the accuracy of optimization models, and our results demonstrate its effectiveness in generating optimal portfolios in the market.