making decision on selection of optimal stock portfolio employing meta heuristic algorithms for multi-objective functions subject to real-life constraints

The purpose of this study is to utilize data envelopment analysis and metaheuristic algorithms to make investment decisions and select an optimal stock portfolio, considering real-life constraints and multi-objective functions. the statistical population for this research comprises 183 selected companies from the tehran stock exchange involved in capital decision-making and optimal capital composition. eventually, 42 companies were identified as justifiable investment options. after assessing the risk and return of efficient companies, the study formulated a multi-objective model based on the investor's budget limitations, requirements, and expectations to determine the investment composition. to achieve optimal decisions, a modified genetic metaheuristic algorithm and matlab software with dual operators were employed. sensitivity analysis revealed that eliminating the risk minimization function enhanced the decision's return level but increased risk. conversely, eliminating the maximizing return function improved decision-making risk but reduced investment return. eliminating investment requirements and expectations increased returns and investment risk while involving more companies in the optimal investment portfolio.