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MÜHENDİSLİK TASARIM PROBLEMLERİNİ ÇÖZMEK İÇİN KISIT-YÖNETİMİ MEKANİZMALARININ KARŞILAŞTIRMALI BİR ANALİZİ

Yıl 2021, Cilt: 32 Sayı: 2, 201 - 216, 31.08.2021
https://doi.org/10.46465/endustrimuhendisligi.826148

Öz

Optimizasyon problemlerinin bilim ve mühendislikte çok sayıda gerçek yaşam uygulaması vardır. Mühendislik tasarım problemleri genellikle çeşitli kısıtlamalara tabidir. Son on yılda birçok modern meta-sezgisel optimizasyon algoritması geliştirilmiş olsa da bu algoritmalar, kısıtlı optimizasyon problemleriyle başa çıkmak için ek kısıt-yönetimi mekanizmaları gerektirir. Bu nedenle, uygun bir kısıt-yönetimi mekanizmasının seçilmesi, zaman alıcı ve zorlu olan kapsamlı deneme yanılma deneyleri gerektirir. Bu çalışmada, karar vericilere optimizasyon uygulamalarında yol gösterecek şekilde sekiz kısıt-yönetimi mekanizmasının karşılaştırmalı bir analizi gerçekleştirilmiştir. Kısıt-yönetimi teknikleri, balina optimizasyon algoritmasıyla birlikte kullanılmış ve deneysel analizde yine CEC2020 kıyaslama paketinin bir parçası olan 19 gerçek hayat mekanik tasarım problemi test edilmiştir. Nemenyi ve Holm post-hoc prosedürlerini içeren nonparametrik istatistiksel analiz, ters tanjant kısıt-yönetimi ve eklektik ceza yöntemlerinin gerçek hayattaki mekanik tasarım problemlerinde yüksek performans sergilediğini göstermektedir.

Kaynakça

  • Andrei, N., & Andrei, N. (2013). Nonlinear optimization applications using the GAMS technology: Springer.
  • Arora, J. S. (2004). Introduction to optimum design: Elsevier.
  • Beightler, C. S., & Phillips, D. T. (1976). Applied geometric programming: John Wiley & Sons.
  • Belegundu, A. D., & Arora, J. S. (1985). A study of mathematical programming methods for structural optimization. Part I: Theory. International Journal for Numerical Methods in Engineering, 21(9), 1583-1599.
  • Carlson, S. E., & Shonkwiler, R. (1998, 14-14 Oct. 1998). Annealing a genetic algorithm over constraints. Paper presented at the IEEE International Conference on Systems, Man, and Cybernetics
  • Chew, S. H., & Zheng, Q. (2012). Integral Global Optimization: Theory, Implementation and Applications (Vol. 298): Springer Science & Business Media.
  • Coello Coello, C. A. (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), 1245-1287. doi:https://doi.org/10.1016/S0045-7825(01)00323-1
  • Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2), 311-338. doi:https://doi.org/10.1016/S0045-7825(99)00389-8
  • Derrac, J., García, 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. doi:https://doi.org/10.1016/j.swevo.2011.02.002
  • Gölcük, İ., & Ozsoydan, F. B. (2020). Evolutionary and adaptive inheritance enhanced Grey Wolf Optimization algorithm for binary domains. Knowledge-Based Systems, 105586. doi:https://doi.org/10.1016/j.knosys.2020.105586
  • Grandhi, R. (1993). Structural optimization with frequency constraints-a review. AIAA journal, 31(12), 2296-2303.
  • Gupta, S., Tiwari, R., & Nair, S. B. (2007). Multi-objective design optimisation of rolling bearings using genetic algorithms. Mechanism and Machine Theory, 42(10), 1418-1443.
  • Hadj-Alouane, A. B., & Bean, J. C. (1997). A Genetic Algorithm for the Multiple-Choice Integer Program. Operations Research, 45(1), 92-101. doi:10.1287/opre.45.1.92 Himmelblau, D. M. (2018). Applied nonlinear programming: McGraw-Hill.
  • Homaifar, A., Qi, C. X., & Lai, S. H. (1994). Constrained Optimization Via Genetic Algorithms. SIMULATION, 62(4), 242-253. doi:10.1177/003754979406200405
  • Joines, J. A., & Houck, C. R. (1994, 27-29 June 1994). On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. Paper presented at the Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.
  • Kim, T. H., Maruta, I., & Sugie, T. (2010). A simple and efficient constrained particle swarm optimization and its application to engineering design problems. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 224(2), 389-400. doi:10.1243/09544062JMES1732
  • Kumar, A., Wu, G., Ali, M. Z., Mallipeddi, R., Suganthan, P. N., & Das, S. (2020a). Guidelines for real-world single-objective constrained optimisation competition. Retrieved from https://github.com/P-N-Suganthan/2020-RW-Constrained-Optimisation
  • Kumar, A., Wu, G., Ali, M. Z., Mallipeddi, R., Suganthan, P. N., & Das, S. (2020b). A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm and Evolutionary Computation, 56, 100693. doi:https://doi.org/10.1016/j.swevo.2020.100693
  • Mallipeddi, R., & Suganthan, P. N. (2010). Ensemble of constraint handling techniques. IEEE Transactions on Evolutionary Computation, 14(4), 561-579.
  • Mezura-Montes, E., & Coello Coello, C. A. (2011). Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation, 1(4), 173-194. doi:https://doi.org/10.1016/j.swevo.2011.10.001
  • Michalewicz, Z., & Schoenauer, M. (1996). Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation, 4(1), 1-32. doi:10.1162/evco.1996.4.1.1
  • Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. doi:https://doi.org/10.1016/j.advengsoft.2016.01.008
  • Morales, A., & Quezada, C. (1998). A univeral eclectic genetic algorithm for constrained optimization. Paper presented at the 6th European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany.
  • Nowacki, H. (1973). Optimization in pre-contract ship design. Paper presented at the International Conference on Computer Applications in the Automation of Shipyard Operation and Ship Design
  • Osyczka, A., Krenich, S., & Karas, K. (1999). Optimum design of robot grippers using genetic algorithms. Paper presented at the Proceedings of the Third World Congress of Structural and Multidisciplinary Optimization (WCSMO), Buffalo, New York.
  • Ragsdell, K. M., & Phillips, D. T. (1976). Optimal design of a class of welded structures using geometric programming. ASME. J. Eng. Ind., 98(3), 1021-1025.
  • Rao, S. S. (1996). Further topics in optimization. In Engineering Optimization: Theory and Practice (pp. 779-783): New Age International Publishers.
  • Sandgren, E. (1988). Nonlinear integer and discrete programming in mechanical design. Paper presented at the Proceeding of the ASME design technology conference.
  • Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. J. Mechn. Des., 112, 223-229.
  • Siddall, J. N. (1982). Optimal engineering design: principles and applications: CRC Press.
  • Sigmund, O. (2001). A 99 line topology optimization code written in Matlab. Structural and Multidisciplinary Optimization, 21(2), 120-127.
  • Simon, D. (2013). Evolutionary optimization algorithms: John Wiley & Sons.
  • Steven, G. (2002). Evolutionary algorithms for single and multicriteria design optimization. A. Osyczka. Springer Verlag, Berlin, 2002, ISBN 3-7908-1418-01. Structural and Multidisciplinary Optimization, 24(1), 88-89.
  • Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82.
  • Yokota, T., Taguchi, T., & Gen, M. (1998). A solution method for optimal weight design problem of the gear using genetic algorithms. Computers & Industrial Engineering, 35(3-4), 523-526.

A COMPARATIVE ANALYSIS OF CONSTRAINT-HANDLING MECHANISMS FOR SOLVING ENGINEERING DESIGN PROBLEMS

Yıl 2021, Cilt: 32 Sayı: 2, 201 - 216, 31.08.2021
https://doi.org/10.46465/endustrimuhendisligi.826148

Öz

Optimization problems have numerous real-life applications in science and engineering. The engineering design problems are usually subject to various constraints. Although many state-of-the-art metaheuristic optimization algorithms have been developed during the last decades, these algorithms require additional constraint-handling mechanisms to cope with constrained optimization problems. Therefore, selecting a suitable constraint-handling mechanism requires extensive trial-and-error experiments, which is time-consuming and demanding. In this study, a comparative analysis of the eight constraint handling mechanisms is carried out, guiding decision-makers in their optimization practices. The constraint-handling techniques are used along with the whale optimization algorithm, and 19 real-life mechanical design problems, which are also part of the CEC2020 benchmark suite, are tested in the experimental analysis. The nonparametric statistical analysis incorporating Nemenyi and Holm post-hoc procedures shows that the inverse tangent constraint-handling and eclectic penalty methods exhibit high performance in real-life mechanical design problems.

Kaynakça

  • Andrei, N., & Andrei, N. (2013). Nonlinear optimization applications using the GAMS technology: Springer.
  • Arora, J. S. (2004). Introduction to optimum design: Elsevier.
  • Beightler, C. S., & Phillips, D. T. (1976). Applied geometric programming: John Wiley & Sons.
  • Belegundu, A. D., & Arora, J. S. (1985). A study of mathematical programming methods for structural optimization. Part I: Theory. International Journal for Numerical Methods in Engineering, 21(9), 1583-1599.
  • Carlson, S. E., & Shonkwiler, R. (1998, 14-14 Oct. 1998). Annealing a genetic algorithm over constraints. Paper presented at the IEEE International Conference on Systems, Man, and Cybernetics
  • Chew, S. H., & Zheng, Q. (2012). Integral Global Optimization: Theory, Implementation and Applications (Vol. 298): Springer Science & Business Media.
  • Coello Coello, C. A. (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), 1245-1287. doi:https://doi.org/10.1016/S0045-7825(01)00323-1
  • Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2), 311-338. doi:https://doi.org/10.1016/S0045-7825(99)00389-8
  • Derrac, J., García, 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. doi:https://doi.org/10.1016/j.swevo.2011.02.002
  • Gölcük, İ., & Ozsoydan, F. B. (2020). Evolutionary and adaptive inheritance enhanced Grey Wolf Optimization algorithm for binary domains. Knowledge-Based Systems, 105586. doi:https://doi.org/10.1016/j.knosys.2020.105586
  • Grandhi, R. (1993). Structural optimization with frequency constraints-a review. AIAA journal, 31(12), 2296-2303.
  • Gupta, S., Tiwari, R., & Nair, S. B. (2007). Multi-objective design optimisation of rolling bearings using genetic algorithms. Mechanism and Machine Theory, 42(10), 1418-1443.
  • Hadj-Alouane, A. B., & Bean, J. C. (1997). A Genetic Algorithm for the Multiple-Choice Integer Program. Operations Research, 45(1), 92-101. doi:10.1287/opre.45.1.92 Himmelblau, D. M. (2018). Applied nonlinear programming: McGraw-Hill.
  • Homaifar, A., Qi, C. X., & Lai, S. H. (1994). Constrained Optimization Via Genetic Algorithms. SIMULATION, 62(4), 242-253. doi:10.1177/003754979406200405
  • Joines, J. A., & Houck, C. R. (1994, 27-29 June 1994). On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. Paper presented at the Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.
  • Kim, T. H., Maruta, I., & Sugie, T. (2010). A simple and efficient constrained particle swarm optimization and its application to engineering design problems. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 224(2), 389-400. doi:10.1243/09544062JMES1732
  • Kumar, A., Wu, G., Ali, M. Z., Mallipeddi, R., Suganthan, P. N., & Das, S. (2020a). Guidelines for real-world single-objective constrained optimisation competition. Retrieved from https://github.com/P-N-Suganthan/2020-RW-Constrained-Optimisation
  • Kumar, A., Wu, G., Ali, M. Z., Mallipeddi, R., Suganthan, P. N., & Das, S. (2020b). A test-suite of non-convex constrained optimization problems from the real-world and some baseline results. Swarm and Evolutionary Computation, 56, 100693. doi:https://doi.org/10.1016/j.swevo.2020.100693
  • Mallipeddi, R., & Suganthan, P. N. (2010). Ensemble of constraint handling techniques. IEEE Transactions on Evolutionary Computation, 14(4), 561-579.
  • Mezura-Montes, E., & Coello Coello, C. A. (2011). Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation, 1(4), 173-194. doi:https://doi.org/10.1016/j.swevo.2011.10.001
  • Michalewicz, Z., & Schoenauer, M. (1996). Evolutionary Algorithms for Constrained Parameter Optimization Problems. Evolutionary Computation, 4(1), 1-32. doi:10.1162/evco.1996.4.1.1
  • Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. doi:https://doi.org/10.1016/j.advengsoft.2016.01.008
  • Morales, A., & Quezada, C. (1998). A univeral eclectic genetic algorithm for constrained optimization. Paper presented at the 6th European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany.
  • Nowacki, H. (1973). Optimization in pre-contract ship design. Paper presented at the International Conference on Computer Applications in the Automation of Shipyard Operation and Ship Design
  • Osyczka, A., Krenich, S., & Karas, K. (1999). Optimum design of robot grippers using genetic algorithms. Paper presented at the Proceedings of the Third World Congress of Structural and Multidisciplinary Optimization (WCSMO), Buffalo, New York.
  • Ragsdell, K. M., & Phillips, D. T. (1976). Optimal design of a class of welded structures using geometric programming. ASME. J. Eng. Ind., 98(3), 1021-1025.
  • Rao, S. S. (1996). Further topics in optimization. In Engineering Optimization: Theory and Practice (pp. 779-783): New Age International Publishers.
  • Sandgren, E. (1988). Nonlinear integer and discrete programming in mechanical design. Paper presented at the Proceeding of the ASME design technology conference.
  • Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. J. Mechn. Des., 112, 223-229.
  • Siddall, J. N. (1982). Optimal engineering design: principles and applications: CRC Press.
  • Sigmund, O. (2001). A 99 line topology optimization code written in Matlab. Structural and Multidisciplinary Optimization, 21(2), 120-127.
  • Simon, D. (2013). Evolutionary optimization algorithms: John Wiley & Sons.
  • Steven, G. (2002). Evolutionary algorithms for single and multicriteria design optimization. A. Osyczka. Springer Verlag, Berlin, 2002, ISBN 3-7908-1418-01. Structural and Multidisciplinary Optimization, 24(1), 88-89.
  • Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82.
  • Yokota, T., Taguchi, T., & Gen, M. (1998). A solution method for optimal weight design problem of the gear using genetic algorithms. Computers & Industrial Engineering, 35(3-4), 523-526.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

İlker Gölcük 0000-0002-8430-7952

Yayımlanma Tarihi 31 Ağustos 2021
Kabul Tarihi 31 Mayıs 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 32 Sayı: 2

Kaynak Göster

APA Gölcük, İ. (2021). A COMPARATIVE ANALYSIS OF CONSTRAINT-HANDLING MECHANISMS FOR SOLVING ENGINEERING DESIGN PROBLEMS. Endüstri Mühendisliği, 32(2), 201-216. https://doi.org/10.46465/endustrimuhendisligi.826148

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