selection constraint

Since the problem of optimizing the stock portfolio in nonlinear conditions with multi-objective functions cannot be effectively solved using traditional and traditional approaches, in this research, a metaheuristic approach is proposed to optimize the stock portfolio using the particle swarm optimization technique. This method has been implemented on a variety of investment portfolios with selection constraint and unconstraint conditions for the investor. By implementing the algorithm and in order to validate it, the results were compared with the genetic optimization algorithm. The computational results indicate that particle swarm optimization algorithm has a good performance, so that the results seem very promising and satisfactory for use in real conditions.