Credit portfolio management using two-level particle swarm optimization
In this paper, we propose a novel Two-level Particle Swarm Optimization (TLPSO) to solve the credit portfolio management problem. A two-date credit portfolio management model is considered. The objective of the manager is to minimize the maximum expected loss of the portfolio subject to a given consulting budget constraint. The captured problem is very challenging due to its hierarchical structure and its time complexity, so the TLPSO is designed for the credit portfolio management model. The TLPSO has two searching processes, namely, internal-search, the searching process of the maximization problem and external-search, the searching process of the minimization problem. The performance of TLPSO is then compared with both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO), in terms of efficient frontiers, fitness values, convergence rates, computational time consumption and reliability. The experiment results show that TLPSO is more efficient and reliable for the credit portfolio management problem than the other tested methods.
credit portfolio management two-level particle swarm optimization particle swarm optimization genetic algorithm