MULTIOBJECTIVE EVOLUTIONARY METAHEURISTIC APPROACH TO THE CONSTRAINED PORTFOLIO OPTIMIZATION PROBLEM

In this paper, we propose a multi-objective evolutionary metaheuristic approach based on the Pareto Ant Colony Optimization (P-ACO) and non-dominated genetic sorting algorithms (NSGA II NSGA III) to solve bi-objective portfolio optimization problem. P-ACO is used select best assets composing efficient portfolio. Then, III are separately find proportional weights of budget allocated selected The results obtained by these two were compared designate performing algorithm. Finally, performed another comparison between our those an exact method for same numerical experiments set instances from literature revealed that combination ant colony algorithm proposed most often gave much better than both one hand iterative other hand.