Application of Quantum-Behaved Particle Swarm Optimization in Engineering Constrained Optimization Problems

Abstract:

To overcome the shortage of standard Particle Swarm Optimization(SPSO) on premature convergence, Quantum-behaved Particle Swarm Optimization (QPSO) is presented to solve engineering constrained optimization problem. QPSO algorithm is a novel PSO algorithm model in terms of quantum mechanics. The model is based on Delta potential, and we think the particle has the behavior of quanta. Because the particle doesn’t have a certain trajectory, it has more randomicity than the particle which has fixed path in PSO, thus the QPSO more easily escapes from local optima, and has more capability to seek the global optimal solution. In the period of iterative optimization, outside point method is used to deal with those particles that violate the constraints. Furthermore, compared with other intelligent algorithms, the QPSO is verified by two instances of engineering constrained optimization, experimental results indicate that the algorithm performs better in terms of accuracy and robustness.

• Periodical: Advanced Materials Research (Volumes 383-390)
• Pages: 7208-7213
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.383-390.7208
• Online since: November 2011
• Authors: De Kun Tan
• Keywords: Constrained Optimization, External Point Method, Particle Swarm Optimization Algorithm (PSO), Quantum-Behaved Particle Swarm Optimization Algorithm

References

[1] Y. L. Gao, H. R. Li, Hybrid Particle Swarm Algorithm of Nonlinear Constraint Optimization Problems, Chinese Journal of Computational Mathematics, vol. 32, No. 2, 2010, PP. 135-146.

[2] C. C. Liao, G. Xi, J. Z. Xu, An Improved PSO Algorithm for Solution of Constraint Optimization Problem and Its Application, Chinese Journal of Engineering Thermophysics, Vol. 31, No. 1, 2010, PP. 32-35.

[3] J. Sun, B. Feng, W.B. Xu, Particle swarm optimization with particles having quantum behavior, Proceedings of 2004 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2004, PP. 325-331.

DOI: 10.1109/cec.2004.1330875

[4] D. K. Tan, W. H. Luo, Q. Liu, Multi-objective particle swarm optimization algorithm for engineering constrained optimization problems,. Proceedings of 2009 conference on granular computing, Piscataway, NJ: IEEE Press, 2009, PP. 523-528.

DOI: 10.1109/grc.2009.5255064

[5] J. Sun, B. Feng, W. B. Xu, A Global Search Strategy ofQuantum-behaved Particle Swarm Optimization, Proceedings of IEEE conference on Cybernetics and Intelligent Systems, Piscataway, NJ: IEEE Press, PP. 111-116.

DOI: 10.1109/iccis.2004.1460396

[6] W. Fang, J. Sun, W. B. Xu, Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Differential Evolution Operator and Its Application, Chinese Journal of System Simulation, Vol. 20, No. 24, 2008, PP. 6740-6744.

[7] C. G. Zhang, L. Y. Xu, H. H. Shao, Improved Chaos Optimization Algorithm and Its Application in Nonlinear Constraint Optimization Problems, Journal of Shanghai Jiaotong University, Vol. 34, No. 5, 2000, PP. 593-599.

[8] Deb K, An efficient constraint handing method for genetic algorithms, Computer Methods in Applied Mechanics and Engineering, Vol. 186, No. 2, 2000, PP. 311-338.

DOI: 10.1016/s0045-7825(99)00389-8

[9] CARLOS A, Use of self-adaptive penalty approach for engineering optimization problems, Computer in Industry, No. 41, 2000, PP. 113-127.

[10] B. K. KANNAN, S. N. KRAMER, An augmented Lagrange multiplier based method for mixed integer discrete continous optimization and its applications to mechanical design, Journal of Mechanical Design, Vol. 116, No. 2, 1994, PP. 318-322.

DOI: 10.1115/1.2919393

[11] L. Z. Chen, Mechanical Optimization Design Method, Metallurgical Industry Press, Beijing China, (1997).