Authors:
Khin Lwin
1
;
Rong Qu
1
and
Jianhua Zheng
2
Affiliations:
1
School of Computer Science and University of Nottingham, United Kingdom
;
2
University of Nottingham, United Kingdom
Keyword(s):
Evolutionary Multi-objective Portfolio Optimization, Hybrid Metaheuristic, Multi-objective Scatter Search, Cardinality Constrained Portfolio Selection Problem, Mean-Variance Portfolio Optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolution Strategies
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Soft Computing
Abstract:
The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multiobjective Scatter Search approach. In this work, we present a hybrid multiobjective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection problem. We considered the extended mean-variance 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 some 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 impro
ved portfolios. The performance of the proposed multiobjective Scatter Search algorithm is compared with Non-dominated Sorting Genetic Algorithm (NSGA-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 on the solution quality within a shorter computational time.
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