A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems

Authors

  • D B Fatemeh Department of Artificial Intelligence,Faculty of Computer Science & Information Technology,University of Malaya, 50603 Malaysia
  • C K Loo Advanced Robotics Lab Department of Artificial Intelligence Faculty of Computer Science and Information Technology, University of Malaya, Malaysia
  • G Kanagaraj Department of Mechatronics Engineering, Thiagarajar College of Engineering, Madurai, India
  • S G Ponnambalam Faculty of Manufacturing Engineering University Malaysia Pahang 26600 Pekan, Pahang, Malaysia

DOI:

https://doi.org/10.15282/jmmst.v1i1.195

Keywords:

Constrained optimization, Hybrid algorithm, Swarm intelligence, Penalty method

Abstract

Most real-life optimization problems involve constraints which require a specialized mechanism to deal with them. The presence of constraints imposes additional challenges to the researchers motivated towards the development of new algorithm with efficient constraint handling mechanism. This paper attempts the suitability of newly developed hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviated as SP-QPSO, extended specifically designed for solving constrained optimization problems. The incorporation of adaptive penalty method guides the solutions to the feasible regions of the search space by computing the violation of each one. Further, the algorithm’s performance is improved by Centroidal Voronoi Tessellations method of point initialization promise to visit the entire search space. The effectiveness and the performance of SP-QPSO are examined by solving a broad set of ten benchmark functions and four engineering case study problems taken from the literature. The experimental results show that the hybrid version of SP-QPSO algorithm is not only overcome the shortcomings of the original algorithms but also outperformed most state-of-the-art algorithms, in terms of searching efficiency and computational time.

References

Sayed, G. I., Darwish, A., & Hassanien, A. E. (2018). A new chaotic multi-verse optimization algorithm for solving engineering optimization problems. Journal of Experimental & Theoretical Artificial Intelligence, 30(2), 293-317.

Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10(2), 629-640.

Coello, C. A. C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer methods in applied mechanics and engineering, 191(11-12), 1245-1287.

Michalewicz, Z., & Schoenauer, M. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary computation, 4(1), 1-32.

Michalewicz, Z., Deb, K., Schmidt, M., & Stidsen, T. (2000). Test-case generator for nonlinear continuous parameter optimization techniques. IEEE Transactions on Evolutionary Computation, 4(3), 197-215.

He, S., Prempain, E., & Wu, Q. H. (2004). An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization, 36(5), 585-605.

Kaveh, A., & Talatahari, S. (2009). Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers & Structures, 87(5-6), 267-283.

Dong, Y., Tang, J., Xu, B., & Wang, D. (2005). An application of swarm optimization to nonlinear programming. Computers & Mathematics with Applications, 49(11-12), 1655-1668.

Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5). New York: Springer.

Das, G., Panda, S., & Padhy, S. K. (2018). Quantum Particle Swarm Optimization Tuned Artificial Neural Network Equalizer. In Soft Computing: Theories and Applications (pp. 579-585). Springer, Singapore.

Taherzadeh, G., & Loo, C. K. (2014, October). Comparison of Applying Centroidal Voronoi Tessellations and Levenberg-Marquardt on Hybrid SP-QPSO Algorithm for High Dimensional Problems. In International Conference in Swarm Intelligence (pp. 332-341). Springer, Cham.

Kennedy, J., & Eberhart, R. C. (1997, October). A discrete binary version of the particle swarm algorithm. In Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on (Vol. 5, pp. 4104-4108). IEEE.

Kaveh, A., Bakhshpoori, T., & Afshari, E. (2015). Hybrid PSO and SSO algorithm for truss layout and size optimization considering dynamic constraints. Structural Engineering and Mechanics, 54(3), 453-474.

He, Q., & Wang, L. (2007). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied mathematics and computation, 186(2), 1407-1422.

Fan, S. K. S., & Zahara, E. (2007). A hybrid simplex search and particle swarm optimization for unconstrained optimization. European Journal of Operational Research, 181(2), 527-548.

Zahara, E., & Hu, C. H. (2008). Solving constrained optimization problems with hybrid particle swarm optimization. Engineering Optimization, 40(11), 1031-1049.

Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10(2), 629-640.

Cagnina, L. C., Esquivel, S. C., & Coello Coello, C. A. (2011). Solving constrained optimization problems with a hybrid particle swarm optimization algorithm. Engineering Optimization, 43(8), 843–866.

Kanagaraj, G., Ponnambalam, S. G., & Jawahar, N. (2013). A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems. Computers & Industrial Engineering, 66(4), 1115-1124.

Xin, B., Chen, J., Zhang, J., Fang, H., & Peng, Z. H. (2012). Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: a review and taxonomy. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(5), 744-767.

Sun, J., Feng, B., & Xu, W. (2004, June). Particle swarm optimization with particles having quantum behavior. In Evolutionary Computation, 2004. CEC2004. Congress on (Vol. 1, pp. 325-331). IEEE.

Wu, S. (2018). A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism. International Journal of Swarm Intelligence Research (IJSIR), 9(1), 1-19.

Fu, Y., Ding, M., & Zhou, C. (2012). Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE transactions on systems, man and cybernetics, part A: systems and humans, 42(2), 511-526.

Mezura-Montes, E., & Coello, C. A. C. (2011). Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm and Evolutionary Computation, 1(4), 173-194.

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Published

13-09-2018

How to Cite

Fatemeh, D. B., Loo, C. K., Kanagaraj, G., & Ponnambalam, S. G. (2018). A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems. Journal of Modern Manufacturing Systems and Technology, 1, 15–26. https://doi.org/10.15282/jmmst.v1i1.195

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Articles