Opposition-Based Barebones Particle Swarm for Constrained Nonlinear Optimization Problems

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.