Multi-objective Scatter Search with External Archive for Portfolio Optimization

The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multi-objective Scatter Search approach. In this work, we present a hybrid multi-objective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection problem. We considered the extended meanvariance portfolio model with three practical constraints which limit the number of assets in a portfolio, restrict the proportions of assets held in the portfolio and pre-assign specific assets in the portfolio. The proposed hybrid metaheuristic algorithm follows the basic structure of the Scatter Search and defines the reference set solutions based on Pareto dominance and crowding distance. New Subset generation and combination methods are proposed to generate efficient and diversified portfolios. Hill Climbing operation is integrated to search for improved portfolios. The performance of the proposed multi-objective Scatter Search algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-2) and Pareto Envelope-based Selection Algorithm (PESA-II). Experimental results indicate that the proposed algorithm is a promising approach for solving the constrained portfolio selection problem. Measurements by the performance metrics indicate that it outperforms NSGA-II, SPEA2 and PESA-II on the solution quality within a shorter computational time.