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 managem ent 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 comp lexity, so the TLPSO is designed for the credit portfolio management model. The TLPSO has two searching processes, namely, ‘‘internal-sea rch’’, 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 Optimizatio n (PSO), in terms of efficient frontiers, fitness values, convergence rates, computatio nal time consumpt ion and reliability. The experimen t results show that TLPSO is more efficient and reliable for the credit portfolio management problem than the other tested methods. 2013 Elsevier Inc. All rights reserved.