Particle swarm optimization for bipartite subgraph problem: a case study

Authors:
Dan Zhang;Zeng-Zhi Li;Hong Song;Tao Zhan
Affiliations:
School of Electronics & Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China;School of Electronics & Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China;School of Mechanical Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, China;Dept. of Computer Science & Engineering, Northwest Polytechnical University, Xi'an, Shaanxi, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

The goal of bipartite subgraph problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. This paper proposes a modified particle swarm optimization (PSO), called MPPSO (Mutated Personalized PSO), for this NP-hard problem. The proposed MPPSO algorithm contains a key improvement by introducing a personality factor from a psychological standpoint and a mutation operator for global best. Additionally the symmetry issue of solution space of bipartite subgraph problem is coped well with too. A large number of instances have been simulated to verify the proposed algorithm. The results show that the personality factor and mutation operator are efficient and the quality of our algorithm is superior to those of the existing algorithms.