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Journal of Intelligent Manufacturing

Erschienen in:

29.01.2016

Engineering design optimization using an improved local search based epsilon differential evolution algorithm

verfasst von: Wenchao Yi, Yinzhi Zhou, Liang Gao, Xinyu Li, Chunjiang Zhang

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 7/2018

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Abstract

Many engineering problems can be categorized into constrained optimization problems (COPs). The engineering design optimization problem is very important in engineering industries. Because of the complexities of mathematical models, it is difficult to find a perfect method to solve all the COPs very well. \(\varepsilon \) constrained differential evolution (\(\varepsilon \)DE) algorithm is an effective method in dealing with the COPs. However, \(\varepsilon \)DE still cannot obtain more precise solutions. The interaction between feasible and infeasible individuals can be enhanced, and the feasible individuals can lead the population finding optimum around it. Hence, in this paper we propose a new algorithm based on \(\varepsilon \) feasible individuals driven local search called as \(\varepsilon \) constrained differential evolution algorithm with a novel local search operator (\(\varepsilon \)DE-LS). The effectiveness of the proposed \(\varepsilon \)DE-LS algorithm is tested. Furthermore, four real-world engineering design problems and a case study have been studied. Experimental results show that the proposed algorithm is a very effective method for the presented engineering design optimization problems.

Vorheriger Artikel Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

Nächster Artikel A reactive mitigation approach for managing supply disruption in a three-tier supply chain

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

Artigues, C., & Lopez, P. (2014). Energetic reasoning for energy-constrained scheduling with a continuous resource. Journal of Scheduling. doi:10.1007/s10951-014-0404-y.

Brajevic, I., & Tuba, M. (2013). An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. Journal of Intelligent Manufacturing, 24(4), 729–740.CrossRef

Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4), 311–338.CrossRef

Derrac, J., Garcia, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.CrossRef

Domínguez-Isidro, S., Mezura-Montes, E., & Leguizamon, G. (2013). Memetic differential evolution for constrained numerical optimization problems. In IEEE congress on evolutionary computation (CEC), pp. 2996–3003.

Ellis, M., & Christofides, P. D. (2014). Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems. Control Engineering Practice, 22, 242–251.CrossRef

Flager, F., Soremekun, G., Adya, A., Shea, K., Haymaker, J., & Fischer, M. (2014). Fully constrained design: A general and scalable method for discrete member sizing optimization of steel truss structures. Computers and Structures, 140(30), 55–65.CrossRef

Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2011). Mixed variable structural optimization using firefly algorithm. Computers and Structures, 29(5), 464–483.

Gandomi, A., Yang, H. X., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering Computing, 29, 17–35.CrossRef

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.CrossRef

Gong, W., & Cai, Z. (2008). A multi-objective differential evolution algorithm for constrained optimization. In Congress on evolutionary computation (CEC2008), Hong Kong, 1–6, June, pp. 181–188.

Gong, W. Y., & Cai, Z. H. (2013). Differential evolution with ranking-based mutation operators. IEEE Transactions on Cybernetics, 46(6), 2066–2081.CrossRef

Gu, L., Yang, R. J., Cho, C. H., Makowski, M., Faruque, M., & Li, Y. (2001). Optimization and robustness for crashworthiness. International Journal of Vehicle Design, 26(4), 348–360.CrossRef

Han, B., Zhang, W. J., Lu, X. W., & Lin, Y. Z. (2015). On-line supply chain scheduling for single-machine and parallel-machine configurations with a single customer: Minimizing the makespan and delivery cost. European Journal of Operational Research, 244(3), 704–714.CrossRef

Huang, F. Z., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation, 286(1), 340–356.CrossRef

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

Kanagaraj, G., Ponnnabalam, S. G., Jawahar, N., & Nilakantan, J. M. (2014). An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization. Engineering Optimization, 46(10), 1331–1351.CrossRef

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(2), 318–320.CrossRef

Karaboga, D., & Basturk, B. (2007). Artificial bee colony optimization algorithm for solving constrained optimization problems. LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 4529, 789–798.CrossRef

Liang, J. J., Runarsson, T. P., Mezura-Montes, E., Clerc, M., Suganthan, P. N., Coello Coello, C. A., et al. (2006). Problems definitions and evaluation criteria for the CEC’ 2006 special session on constrained real-parameter optimization. http://www.ntu.edu.sg/home/EPNSugan/cec2006/technicalreport.pdf.

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

Mavrotas, G., & Florios, K. (2013). An improved version of the augmented \(\upvarepsilon \)-constraint method (AUGMECON2) for finding the exact Pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652–9669.

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

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

Montemurro, M., Vincenti, A., & Vannucci, P. (2013). The automatic dynamic penalization method for handling constraints with genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 256, 70–87.CrossRef

Moradi, M. H., & Abedini, M. (2012). A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. International Journal of Electrical Power & Energy Systems, 34(1), 66–74.CrossRef

Naber, A., & Kolisch, R. (2014). MIP models for resource-constrained project scheduling with flexible resource profiles. European Journal of Operation Research, 239(2), 335–348.CrossRef

Nowcki, H. (1973). Optimization in pre-contract ship design. In Y. Fujida, K. Lind, & T. J. Williams (Eds.), Computer applications in the automation of shipyard operation and ship design (Vol. 2, pp. 327–328). New York: Elsevier.

Puzzi, S., & Carpinteri, A. (2008). A double-multiplicative dynamic penalty approach for constraint evolutionary optimization. Structure Multidiscipline Optimization, 35(5), 431–445.CrossRef

Ray, T., & Saini, P. (2001). Engineering design optimization using a swarm with an intelligent information sharing among individuals. Engineering Optimization, 33(6), 735–748.CrossRef

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

Rao, R. V., & Pawar, P. J. (2010). Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Applied Soft Computing, 10(2), 445–456.CrossRef

Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.CrossRef

Storn, R. (1999). System design by constraint adaptation and differential evolution. IEEE Transactions on Evolutionary Computation, 3(1), 22–34.CrossRef

Takahama, T., & Sakai, S. (2006). Constrained optimization by the \(\upvarepsilon \)-constrained differential evolution with gradient-based mutation and feasible elites. In IEEE congress on evolutionary computation (CEC2006), Vancouver, BC, Canada, 16–21 July, pp. 308–315.

Takahama, T., & Sakai, S. (2010a). Constrained optimization by the \(\upvarepsilon \) constrained differential evolution with an archive and gradient-based mutation. In IEEE congress on evolutionary computation (CEC 2010), pp. 1–9.

Takahama, T., & Sakai, S. (2010b). Efficient constrained optimization by the \(\upvarepsilon \) constrained adaptive differential evolution. In IEEE congress on evolutionary computation (CEC 2010), pp. 1–8.

Takahama, T., & Sakai, S. (2012). Efficient constrained optimization by the \(\upvarepsilon \) constrained rank-based differential evolution. In IEEE congress on evolutionary computation (CEC 2012), pp. 1–8.

Takahama, T., & Sakai, S. (2013). Efficient constrained optimization by the \(\upvarepsilon \) constrained differential evolution with rough approximation using kernel regression. In IEEE congress on evolutionary computation (CEC 2013), pp. 1334–1341.

Tessema, B., & Yen, G. (2009). An adaptive penalty formulation for constrained evolutionary optimization. IEEE Transactions on Systems, 39(3), 565–578.

Tsai, J. (2005). Global optimization of nonlinear fractional programming problems in engineering design. Engineering Optimization, 37(4), 399–409.CrossRef

Wang, J. W., Wang, H. F., Ip, W. H., Furuta, K., & Zhang, W. J. (2013). Predatory search strategy based on swarm intelligence for continuous optimization problems. Mathematical Problems in Engineering. doi:10.1155/2013/749256.

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

Wang, Y., & Cai, Z. X. (2012). Combining multi-objective optimization with differential evolution to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation, 16(1), 117–134.CrossRef

Yi, W. C., Li, X. Y., Gao., L., & Zhou, Y. Z. (2015). \(\upvarepsilon \) constrained differential evolution algorithm with a novel local search operator for constrained optimization problems. In Proceedings in adaptation, learning and optimization, pp. 495–507.

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

Zhou, Y. Z., Li, X. Y., & Gao, L. (2013). A differential evolution algorithm with intersect mutation operator. Applied Soft Computing, 13(1), 390–401.CrossRef

Zou, D. X., Liu, H. K., Gao, L. Q., & Li, S. (2011). A novel modified differential evolution algorithm for constrained optimization problems. Computers & Mathematics with Applications, 61(6), 1608–1623.CrossRef

Titel: Engineering design optimization using an improved local search based epsilon differential evolution algorithm
verfasst von: Wenchao Yi
Yinzhi Zhou
Liang Gao
Xinyu Li
Chunjiang Zhang
Publikationsdatum: 29.01.2016
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 7/2018
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-016-1199-9

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