Designing and explaining the portfolio optimization model using censored models and meta-heuristic algorithm

One of the important topics discussed in the stock market, which should be considered by both natural and legal investors, is choosing an optimal investment portfolio. In this regard, investors are studied in order to select the best portfolio based on risk and return. However, traditional investment methods do not focus on portfolio optimization and only consider the highest return and lowest risk. This research addresses the gap in solving the problem of wide portfolio optimization by comparing answers using more effective and efficient metaheuristic optimization algorithms, thus reducing the probability of error. During this research, metaheuristic optimization methods are well-designed and studied, and then used to optimize the portfolio despite real market limitations. The developed algorithms are all implemented to solve the extended portfolio optimization problem. In this research, more effective and efficient metaheuristic optimization algorithms are used to solve the problem of wide portfolio optimization and by comparing the answers, the probability of error can be almost zero. The stock portfolios formed by the model based on censoring models have more returns and less risk (variance) than the invasive weed algorithm, showing the superiority of the proposed model in comparison to the invasive weed algorithm. The findings of the research have filled the research gap in investment portfolio valuation and demonstrate that the proposed model has effectively considered investment portfolio selection conditions and determined an optimal investment portfolio.