Skip to main content
Log in

ε Constrained differential evolution using halfspace partition for optimization problems

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

There are many efficient and effective constraint-handling mechanisms for constrained optimization problems. However, most of them evaluate all the individuals, including the worse individuals, which waste a lot of fitness evaluations. In this paper, halfspace partition mechanism based on constraint violation values is proposed. Since constraint violation information of individuals in current generation are already known, the positive side of tangent line of one point as positive halfspace is defined. A point is treated as potential point if it locates in the intersect region of two positive halfspaces. Hence, the region includes all these points has greater possibility to obtain smaller constraint violation. Only when the offspring locates in this area, the actual objective function value and constraint violation will be calculated. The estimated worse individuals will be omitted without calculating actual constraint violation and fitness function value. Four engineering optimization and a case study with the grinding optimization process are studied. The experimental results verify the effectiveness of the proposed mechanism.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Arora, J. S. (1989). Introduction to optimum design. New York: McGraw-Hill.

    Google Scholar 

  • Brajevic, I., & Ignjatovic, J. (2019). An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems. Journal of Intelligent Manufacturing, 30(6), 2545–2574.

    Google Scholar 

  • Brest, J. (2009). Constrained real-parameter optimization with ϵ-self-adaptive differential evolution. In Constraint-handling in evolutionary optimization: Studies in computational intelligence series. Springers.

  • Cai, Z., & Wang, Y. (2006). A multiobjective optimization-based evolutionary algorithm for constrained optimization. IEEE Transactions on Evolutionary Computation, 10, 658–675.

    Google Scholar 

  • Coello Coello, C. A. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41, 113–127.

    Google Scholar 

  • Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186, 311–338.

    Google Scholar 

  • Farmani, R., & Wright, J. A. (2003). Self-adaptive fitness formulation for constrained optimization. IEEE Transactions on Evolutionary Computation, 7(5), 445–455.

    Google Scholar 

  • Gao, L., Zhou, Y. Z., Li, X. Y., Pan, Q. K., & Yi, W. C. (2015). Multi-objective optimization based reverse strategy with differential evolution algorithm for constrained optimization problems. Expert Systems with Applications, 42(14), 5976–5987.

    Google Scholar 

  • Gong, W., & Cai, Z. (2008). A multiobjective differential evolution algorithm for constrained optimization. In Proceeding of the 2008 IEEE congress on evolutionary computation (181–188). Hongkong

  • Hamza, N. M., Essam, D. L., & Saker, R. A. (2016). Constraint consensus mutation-based differential evolution for constrained optimization. IEEE Transactions on Evolutionary Computation, 20(3), 447–459.

    Google Scholar 

  • Huang, J. D., Gao, L., & Li, X. Y. (2015). An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining process. Applied Soft Computing, 36, 349–356.

    Google Scholar 

  • Huang, V. L., Qin, A. K., & Suganthan, P. N. (2006). Self-adaptative differential evolution algorithm for constrained real-parameter optimization. In Proceeding of the 2006 IEEE congress on evolutionary computation (pp. 324–331). Vancouver, BC, Canada.

  • Jia, G., Wang, Y., Cai, Z., & Jin, Y. (2013). An improved (μ+λ)-constrained differential evolution for constrained optimization. Information Sciences, 222, 302–322.

    Google Scholar 

  • Kannan, B. K., & Kramer, S. N. (1994). An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Journal of Mechanical Design, 116, 318–320.

    Google Scholar 

  • Karaboga, D. M., & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In Proceeding of theory and applications of fuzzy logic and soft computing (pp. 789–798).

  • Karagoz, S., & Yildiz, A. R. (2017). A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects. International Journal of Vehicle Design, 73(1–3), 179–188.

    Google Scholar 

  • Kiani, M., & Yildiz, A. R. (2016). A comparative study of non-traditional methods for vehicle crashworthiness and NVH Optimization. Archive and Computational Methods Engineering, 23, 723–734.

    Google Scholar 

  • Lampinen, J., & Zelinka, I. (1999a). Mixed integer-discrete-continuous optimization by differential evolution, Part 1: the optimization method. In Proceedings of 5th international mendel conference on soft computing (pp. 71–76). Brno, Czech Republic.

  • Lampinen, J., & Zelinka, I. (1999b). Mixed integer-discrete-continuous optimization by differential evolution, Part 2: a practical example. In Proceedings of 5th international mendel conference on soft computing (pp. 77–81). Brno, Czech Republic.

  • Li, J. Q., Song, M. X., Wang, L., Duan, P. Y., Han, Y. Y., et al. (2019). Hybrid artificial bee colony algorithm for a parallel batching distributed flow-shop problem with deteriorating jobs. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2019.2943606.

    Article  Google Scholar 

  • Liang, J. J., Runarsson, T. P., Mezura-Montes, E., Clerc, M., Suganthan, P. N., et al. (2006). Problem definitions and evaluation criteria for the CEC 2006. Special session on constrained real-parameter optimization, technical report. Nanyang Technological University, Singapore.

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

    Google Scholar 

  • Liu, J., Zhong, W., & Hao, L. (2007). An organizational evolutionary algorithm for numerical optimization. IEEE Transactions on Systems, Man and Cybernetics—Part B: Cybernetics, 37, 1052–1064.

    Google Scholar 

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

    Google Scholar 

  • Mezura-Montes, E., Coello Coello, C. A., Velázquez-Reyes, J., & Muñoz-Dávila, L. (2007). Multiple trial vectors in differential evolution for engineering design. Engineering Optimization, 39, 567–589.

    Google Scholar 

  • Mezura-Montes, E., Miranda-Varela, M. E., & Gomez-Ramon, R. C. (2010). Differential evolution in constrained numerical optimization: An empirical study. Information Sciences, 180, 4223–4262.

    Google Scholar 

  • Mohamed, A. W. (2018). A novel differential evolution algorithm for constrained engineering optimization problems. Journal of Intelligent Manufacturing, 29, 659–692.

    Google Scholar 

  • Mohamed, A. W., & Sabry, H. Z. (2012). Constrained optimization based on modified differential evolution algorithm. Information Sciences, 194, 171–208.

    Google Scholar 

  • Muñoz, A., Hernández, A., & Villa, E. (2009). Continuous constrained optimization with dynamic tolerance using COPSO algorithm. In E. Mezura-Montes (Ed.), Constraint-handling in evolutionary optimization (pp. 1–23). Heidelberg: Springer.

    Google Scholar 

  • Pholdee, N., Bureerat, S., & Yildiz, A. R. (2017). Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame. International Journal of Vehicle Design, 73(1–3), 20–53.

    Google Scholar 

  • Rao, S. S. (1996). Engineering optimization (3rd ed.). Hoboken: Wiley.

    Google Scholar 

  • Ray, T., Singh, H. K., Isaacs, A., & Smith, W. (2009). Infeasibility driven evolutionary algorithm for constrained optimization. In E. Mezura-Montes (Ed.), Constraint-handling in evolutionary optimization: Studies in computational intelligence series (pp. 145–165). Berlin: Springer.

    Google Scholar 

  • Reynoso-Meza, G., Blasco, X., Sanchis, J., & Martínez, M. (2010). Multiobjective optimization algorithm for solving constrained single objective problems. In Proceeding of 2010 congress on evolutionary computation (pp. 3418–3424), Barcelona, Spain.

  • Runarsson, T. P., & Yao, X. (2000). Stochastic ranking for constraint evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4, 284–294.

    Google Scholar 

  • Runarsson, T. P., & Yao, X. (2005). Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Systems and Human, 35, 233–243.

    Google Scholar 

  • Storn, R. (1999). System design by constraint adaptation and differential evolution. IEEE Transactions on Evolutionary Computation, 3, 22–34.

    Google Scholar 

  • Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.

    Google Scholar 

  • Takahama, T., & Sakai, S. (2004). Constrained optimization by α constrained genetic algorithm (αGA). Systems and Computers in Japan, 35, 11–22.

    Google Scholar 

  • Takahama, T., & Sakai, S. (2005). Constrained optimization by constrained particle swarm optimizer with level control. In Proceedings of the 4th IEEE international workshop on soft computing as transdisciplinary science and technology (pp. 1019–1029). Muroran, Japan.

  • Takahama, T., & Sakai, S. (2006). Constrained optimization by the ϵ constrained differential evolution with gradient-based mutation and feasible elites. In Proceeding of 2006 IEEE congress on evolutionary computation (pp. 308–315). Vancouver, BC, Canada.

  • Takahama, T., & Sakai, S. (2009). Solving difficult constrained optimization problems by theϵ constrained differential evolution with gradient-based mutation. In E. Mezura-Montes (Ed.), Constraint-handling in evolutionary optimization: Studies in computational intelligence series (pp. 51–72). Berlin: Springer.

    Google Scholar 

  • Takahama, T., & Sakai, S. (2010). Constrained optimization by the ε-constrained differential evolution with an achieve and gradient-based mutation. In Proceeding of 2010 congress on evolutionary computation (pp. 1680–1688) Barcelona, Spain.

  • Takahama, T., Sakai, S., & Iwane N. (2005). Constrained optimization by the epsilon constrained hybrid algorithm of particle swarm optimization and genetic algorithm. In Advances in artificial intelligence: Lecture notes in artificial intelligence (pp. 389–400). Springer, Berlin.

  • Tessema, B., & Yen, G. G. (2009). An adaptive penalty formulation for con-strained evolutionary optimization. IEEE Transaction on Systems, Man, and Cybernetics—Part A: Systems and Humans, 39, 565–578.

    Google Scholar 

  • Wang, L., & Li, L. (2010). An effective differential evolution with level comparison for constrained engineering design. Structural and Multidisciplinary Optimization, 41, 947–963.

    Google Scholar 

  • Wang, Y., & Cai, Z. (2011). Constrained evolutionary optimization by means of (μ+λ)-differential evolution and improved adaptive trade-off model. Evolutionary Computation, 19, 249–285.

    Google Scholar 

  • Wang, Y., Li, J. P., Xue, X. H., & Wang, B. C. (2019). Utilizing the correlation between constraints and objective function for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2019.2904900.

    Article  Google Scholar 

  • Weise, T. (2009). Global optimization algorithms—Theory and application, 2nd edn.

  • Wen, X. M., Tay, A. A. O., & Nee, A. Y. C. (1992). Microcomputer-based optimization of the surface grinding process. Journal of Materials Processing Technology, 29(1–3), 75–90.

    Google Scholar 

  • Venter, G., & Haftka, R. T. (2010). Constrained particle swarm optimization using a bi-objective formulation. Structural and Multidisciplinary Optimization, 40, 65–76.

    Google Scholar 

  • Yi, W. C., Li, X. Y., Gao, L., Zhou, Y. Z., & Huang, J. D. (2016). ε constrained differential evolution with pre-estimated comparison using gradient-based approximation for constrained optimization problems. Expert Systems with Applications, 44, 37–49.

    Google Scholar 

  • Yi, W. C., Zhou, Y. Z., Gao, L., Li, X. Y., & Zhang, C. J. (2018). Engineering design optimization using an improved local search based epsilon differential evolution algorithm. Journal of Intelligent Manufacturing, 29(7), 1559–1580.

    Google Scholar 

  • Yildiz, A. R. (2013). Comparison of evolutionary based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence, 26(1), 327–333.

    Google Scholar 

  • Yildiz, B. S., & Yildiz, A. R. (2018). Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod. Materials Testing, 60(3), 311–315.

    Google Scholar 

  • Zeng, S., Shi, H., Li, H., Chen, G., Ding, L., et al. (2007). A lower-dimensional-search evolutionary algorithm and its application in constrained optimization problem. In Proceeding of the 2007 IEEE congress on evolutionary computation (pp. 1255–1260). Singapore.

  • Zhang, C., Li, X., Gao, L., & Wu, Q. (2013). An improved electromagnetism-like mechanism algorithm for constrained optimization. Expert Systems with Applications, 40, 5621–5634.

    Google Scholar 

  • Zhang, M., Luo, W., & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178, 3043–3074.

    Google Scholar 

  • Zhang, Q., Zeng, S., Wang, R., Shi, H., Chen, G., et al. (2008). Constrained optimization by the evolutionary algorithm with lower dimensional crossover and gradient-based mutation. In Proceeding of the 2008 congress on evolutionary computation (pp. 273–279). HongKong.

  • Zhang, W., Yen, G., & He, Z. (2014). Constrained optimization via artificial immune system. IEEE Transactions on Cybernetics, 44, 185–198.

    Google Scholar 

  • Zou, D., Liu, H., Gao, L., & Li, S. (2011). A novel modified differential evolution algorithm for constraint optimization problems. Computers and Mathematics with Applications, 61, 1608–1623.

    Google Scholar 

Download references

Acknowledgement

This research work is supported by financial support from the National Natural Science Foundation for Distinguished Young Scholars of China under Grant No. 51825502, National Natural Science of China under Grant Nos. 71371170, 71871203, L1924063. Foundation of Zhejiang Education Committee under Grant No. Y201840056. Zhejiang Natural Science Foundation of China under Grant No. LY18G010017.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liang Gao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yi, W., Gao, L., Pei, Z. et al. ε Constrained differential evolution using halfspace partition for optimization problems. J Intell Manuf 32, 157–178 (2021). https://doi.org/10.1007/s10845-020-01565-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01565-2

Keywords

Navigation