Article: Solving bank debt problems based on parallel NSGA-II algorithm Journal: International Journal of Bio-Inspired Computation (IJBIC) 2025 Vol.25 No.2 pp.88 - 103 Abstract: In order to alleviate the balance of bank liquidity, profitability, and safety, this paper regards bank liability management as a multi-objective optimisation problem, establishes a multi-objective bank liability model with solvency, liquidity risk, and net interest income as the goals, and proposes an improved adaptation value method and environment selection method to improve the NSGA-II algorithm (PNSGA-II) to realise the optimisation of bank asset management. Compared with the NSGA-II algorithm, the PNSGA-II algorithm has better convergence and diversity, so as to better solve the problem of bank liability management. Compared with the NSGA-II algorithm, SPEA2 algorithm, and improved algorithm, the Pareto frontier distribution and IGD index of the PNSGA-II algorithm have better performance, indicating that the proposed algorithm has better convergence and diversity, and better comprehensive performance. The experimental results show that by using the parallel NSGA-II algorithm to solve the bank liability problem, banks can select a realistic set of optimal solutions according to the actual situation among the six sets of Pareto optimal solutions, so as to more conveniently and objectively predict the liability management, asset allocation, and macro-control in the next few years. Inderscience Publishers - linking academia, business and industry through research

Title: Solving bank debt problems based on parallel NSGA-II algorithm

Authors: Xuezhi Yue; Teng Xiong; Wenxing Zhu

Addresses: Faculty of Science, Jiangxi University of Science and Technology, Ganzhou, China ' Faculty of Science, Jiangxi University of Science and Technology, Ganzhou, China ' Business School, Jiangxi University of Science and Technology, Nanchang, China

Abstract: In order to alleviate the balance of bank liquidity, profitability, and safety, this paper regards bank liability management as a multi-objective optimisation problem, establishes a multi-objective bank liability model with solvency, liquidity risk, and net interest income as the goals, and proposes an improved adaptation value method and environment selection method to improve the NSGA-II algorithm (PNSGA-II) to realise the optimisation of bank asset management. Compared with the NSGA-II algorithm, the PNSGA-II algorithm has better convergence and diversity, so as to better solve the problem of bank liability management. Compared with the NSGA-II algorithm, SPEA2 algorithm, and improved algorithm, the Pareto frontier distribution and IGD index of the PNSGA-II algorithm have better performance, indicating that the proposed algorithm has better convergence and diversity, and better comprehensive performance. The experimental results show that by using the parallel NSGA-II algorithm to solve the bank liability problem, banks can select a realistic set of optimal solutions according to the actual situation among the six sets of Pareto optimal solutions, so as to more conveniently and objectively predict the liability management, asset allocation, and macro-control in the next few years.

Keywords: asset liability management; multi-objective optimisation; PNSGA-II; prioritisation.

DOI: 10.1504/IJBIC.2025.145520

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.2, pp.88 - 103

Received: 25 Feb 2023
Accepted: 02 Jun 2023
Published online: 02 Apr 2025 *

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Title: Solving bank debt problems based on parallel NSGA-II algorithm

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