Multi-Objective Portfolio Optimization Using Hybrid Ant Colony Optimization and Compromise Programming

Abstract

The increasing complexity of stock trading requires effective portfolio management to optimize returns while minimizing risks. Portfolio selection is critical in determining the most suitable combination of stocks, aiming to maximize expected returns and minimize risk within a given investment limit. This study constructs a mathematical model for portfolio optimization using six different stocks, incorporating constraints such as expected return, risk, and available investment. Given the multi-objective nature of the problem, a hybrid approach is proposed, combining Compromise Programming (CP), Nadir Compromise Programming (NCP), and Ant Colony Optimization (ACO) to address both minimization and maximization objectives. The ACO algorithm is applied to minimize deviation variables, which serve as the fitness function in the optimization process. The results demonstrate the effectiveness of the hybrid method in selecting portfolios that achieve minimal deviation, providing an optimal balance between risk and return. This research offers valuable insights for investors by illustrating the trade-offs between risk and reward in stock selection, contributing to more informed decision-making in portfolio management.