Paper Info
Title
Improved Multiobjective Bat Algorithm For The Credibilistic Multiperiod Mean-Var Portfolio Optimization Problem
Abstract
This paper deals with a multiperiod multiobjective fuzzy portfolio selectiossn problem based on credibility theory. A credibilistic multiobjective mean-VaR model is formulated for the multiperiod portfolio selection problem, whereby the return is quantified by the credibilistic mean and the risk is measured by the credibilistic VaR. We also consider liquidity, cardinality, and upper and lower bound constraints to obtain a more realistic model. Furthermore, to solve the proposed model efficiently, an improved multiobjective bat algorithm termed IMBA is designed, in which three new strategies, i.e., the global best solution selection strategy, candidate solution generation strategy, and competitive learning strategy, are proposed to increase the convergence speed and improve the solution quality. Finally, comparative experiments are presented to show the applicability and superiority of the proposed approaches from two aspects. First, the designed IMBA is compared with seven typical algorithms, i.e., multiobjective particle swarm optimization, multiobjective artificial bee colony, multiobjective firefly algorithm, multiobjective differential evolution, multiobjective bat, the non-dominated sorting genetic algorithm (NSGA-II) and strength pareto evolutionary algorithm 2 (SPEA2), on a number of benchmark test problems. Second, the applicability of the proposed model to practical applications of portfolio selection is given under different circumstances.
Year
DOI
Venue
2021
10.1007/s00500-021-05638-z
SOFT COMPUTING
Keywords
DocType
Volume
Multiperiod portfolio selection, Credibility measure, Multiobjective optimization, Value at risk, Bat algorithm
Journal
25
Issue
ISSN
Citations 
8
1432-7643
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Manrui Jiang111.70
Weiyi Liu200.68
W. Xu330947.55
Wei Chen41711246.70