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Mathematical Problems in Engineering
Volume 2012, Article ID 761708, 12 pages
Research Article

Opposition-Based Barebones Particle Swarm for Constrained Nonlinear Optimization Problems

School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Received 29 December 2011; Revised 9 April 2012; Accepted 10 May 2012

Academic Editor: Jianming Shi

Copyright © 2012 Hui Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performance of OBPSO, a set of well-known constrained benchmark functions is used in the experiments. Simulation results show that our approach achieves a promising performance.