The Application of Imperialist Competitive Algorithm for Fuzzy Random Portfolio Selection Problem
This work addresses portfolio optimization in finance, but it is incremental as it applies an existing algorithm to a specific problem with fuzzy random variables.
The paper tackled the fuzzy random portfolio selection problem by applying the Imperialist Competitive Algorithm (ICA) to optimize portfolios with asset returns modeled as fuzzy random variables, demonstrating its efficiency through a numerical example.
This paper presents an implementation of the Imperialist Competitive Algorithm (ICA) for solving the fuzzy random portfolio selection problem where the asset returns are represented by fuzzy random variables. Portfolio Optimization is an important research field in modern finance. By using the necessity-based model, fuzzy random variables reformulate to the linear programming and ICA will be designed to find the optimum solution. To show the efficiency of the proposed method, a numerical example illustrates the whole idea on implementation of ICA for fuzzy random portfolio selection problem.