Solving constrained optimization problems with parallel quantum particle swarm optimization
用并行化的QPSO解决有约束的优化问题

In this paper,parallel quantum model of particle swarm system was adopted to enhance the global search ability,and as well as a non-stationary multi-stage assignment penalty in solving constrained problem to improve the convergence and gain more accurate results.Thus,a new optimization algorithm of PQPSO was proposed.This approach was tested on several accredited benchmark functions and the experimental results show much advantage of PQPSO to QPSO(Quantum-behaved Particle Swarm Optimization) and the traditional PSO in terms of optimal value and running time,and the running time is also decreased in linear.