In this research, using Analytic Hierarchy Process (AHP) and TOPSIS method (TOP) based on financial criteria, earnings per share, dividend per share, operating cash flow growth, earnings per share growth, beta (systematic) risk, volatility Operating profit, stock liquidity and price-to-earnings ratio selected according to the opinions of investment experts, formed a portfolio. Then, the criteria for evaluating the performance of risk-adjusted portfolios according to modern portfolio theory (Sharp, Trainer and Alpha Jensen) were calculated based on monthly stock prices during the 5-year period from 1393 to 1397 and com-pared with the criteria for evaluating portfolio performance according to Marko-witz model. The results showed that the selection of stock portfolio using the mentioned financial criteria and using the Analytic Hierarchy Process (AHP) model leads to the acquisition of adjusted returns with more risk than the Marko-witz optimization model and the TOPSIS model (TOP).

The optimization of investment portfolios is the most important topic in financial decision making, and many relevant models can be found in the literature. According to importance of portfolio optimization in this paper, deals with novel solution approaches to solve new developed portfolio optimization model. Contrary to previous work, the uncertainty of future returns of a given portfolio is modeled using LRFUZZY numbers while the function of its return are evaluated using possibility theory. We used a novel Lp-metric method to solve the model. The efficacy of the proposed model is tested on criterion problems of portfolio optimization on LINGO provides a framework to optimize objectives when creating the loan portfoliso, in a search for a dynamic markets decision. In addition to, the performance of the proposed efficiently encoded multi-objective portfolio optimization solver is assessed in comparison with two well-known MOEAs, namely NSGAII and ICA. To the best of our knowledge, there is no research that considered NSGAΠ , ICA fuzzy simultaneously. Due to improve the performance of algorithm, the performance of this approach more study is probed by using a dataset of assets from the Iran’ s stock market for three years historical data and PRE method. The results are analyzed through novel performance parameters RPD method. Thus, the potential of our comparison led to improve different portfolios in different generations.