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Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach

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Abstract

This work addresses the optimization of an engine mount design from a multi-objective scenario. Our methodology is divided into three phases: phase one focuses on data collection through computer simulations. The objectives considered during the analyses are: total mass, first natural frequency and maximum von Mises stress. In phase two, a surrogate model by means of genetic programming is generated for each one of the objectives. Moreover, a local search procedure is incorporated into the overall genetic programming algorithm for improving its performance. Finally, in phase three, instead of steering the search to finding the approximate Pareto front, a local exploration approach based on a change in the weight space is used to lead a search into user defined directions turning the decision making more intuitive.

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References

  • Bakhtiari, H., Karimi, M., & Rezazadeh, S. (2016). Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results. Journal of Intelligent Manufacturing, 27(2), 463–473.

    Google Scholar 

  • Bin, X., Nan, C., & Huajun, C. (2010). An integrated method of multi-objective optimization for complex mechanical structure. Advances in Engineering Software, 41(2), 277–285.

    Google Scholar 

  • Box, M. J., & Draper, N. R. (1971). Factorial designs, the \(|X^{T}X|\) criterion, and some related matters. Technometrics, 13(4), 731–742.

    Google Scholar 

  • Brezocnik, M., Balic, J., & Kuzman, K. (2002). Genetic programming approach to determining of metal materials properties. Journal of Intelligent Manufacturing, 13(1), 5–17.

    Google Scholar 

  • Caydas, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(3), 639–650.

    Google Scholar 

  • Chan, W. L., Fu, M. W., & Lu, J. (2008). An integrated FEM and ANN methodology for metal-formed product design. Engineering Applications of Artificial Intelligence, 21(8), 1170–1181.

    Google Scholar 

  • Chan, K. Y., Kwong, C. K., & Fogarty, T. C. (2010). Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers. Information Sciences, 180(4), 506–518.

    Google Scholar 

  • Chen, Z., Du, F., & Tang, X. (2018). Position and orientation best-fitting based on deterministic theory during large scale assembly. Journal of Intelligent Manufacturing, 29(4), 827–837.

    Google Scholar 

  • Chiandussi, G., Codegone, M., Ferrero, S., & Varesio, F. E. (2012). Comparison of multi-objective optimization methodologies for engineering applications. Computers and Mathematics with Applications, 63(5), 912–942.

    Google Scholar 

  • Christensen, P. W., & Klarbring, A. (2009). An introduction to structural optimization. New York: Springer.

    Google Scholar 

  • Claus Hillermeier, C. (2001). Nonlinear Multiobjective Optimization—A generalized Homotopy Approach. Berlin: Birkhäuser.

    Google Scholar 

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

    Google Scholar 

  • Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. New York: Wiley.

    Google Scholar 

  • Deb, K., Sundar, J., Rao, N., & Chaudhuri, S. (2006). Reference point based multi-objective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research, 2(3), 273–286.

    Google Scholar 

  • Deka, D. J., Sandeep, G., & Chakraborty, D. (2005). Multi-objective optimization of laminated composites using finite element method and genetic algorithm. Journal of Reinforced Plastics and Composites, 24(3), 273–285.

    Google Scholar 

  • Fan, Q., & Yan, X. (2018). Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective p-xylene oxidation process. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-015-1087-8.

    Google Scholar 

  • Feng, C. W., & Huang, H. Y. (2013). Using finite element method and support vector machine to evaluate scour bridge condition. Applied Mechanics and Materials, 330, 900–904.

    Google Scholar 

  • Flores, E. Z., Trujillo, L., Schütze, O., & Legrand, P. (2014). Evaluating the effects of local search in genetic programming. In EVOLVE–A bridge between probability, set oriented numerics, and evolutionay computation V (pp. 213–228).

  • Flores, E. Z., Trujillo, L., Schütze, O., & Legrand, P. (2015). A local search approach to genetic programming for binary classification. In Proceedings of the 2015 annual conference on genetic and evolutionary computation GECCO’15 (pp. 1151–1158).

  • Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35.

    Google Scholar 

  • Garambois, P., Besset, S., & Jezequel, L. (2016). Multi-objective structural robust optimization under stress criteria based on mixed plate super-elements and genetic algorithms. Structural and Multidisciplinary Optimization, 53(2), 205–213.

    Google Scholar 

  • Garg, A., Tai, K., Lee, C. H., & Savalani, M. M. (2014a). A hybrid M5’-genetic programming approach for ensuring greater trustworthiness of prediction ability in modeling of FDM process. Journal of Intelligent Manufacturing, 25, 1349–1365.

    Google Scholar 

  • Garg, A., Tai, K., & Savalani, M. M. (2014b). Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach. The International Journal of Advanced Manufacturing Technology, 73(1–4), 375–388.

    Google Scholar 

  • Garg, A., Tai, K., Vijayaraghavan, V., & Singru, P. M. (2014c). Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach. The International Journal of Advanced Manufacturing Technology, 73(1–4), 113–126.

    Google Scholar 

  • Gass, S., & Saaty, T. (1955). The computational algorithm for the parametric objective function. Naval Research Logistics Quarterly, 2(1–2), 39–45.

    Google Scholar 

  • Ghanmi, S., Guedri, M., Bouazizi, M. L., & Bouhaddi, N. (2007). Use of metamodels in the multi-objective optimization of mechanical structures with uncertainties. International Journal for Computational Methods in Engineering Science and Mechanics, 8(5), 283–302.

    Google Scholar 

  • Ghasemi, F. A., Ghasemi, I., Menbari, S., Ayaz, M., & Ashori, A. (2016). Optimization of mechanical properties of polypropylene/talc/graphene composites using response surface methodology. Polymer Testing, 53, 283–292.

    Google Scholar 

  • Ghosh, A., & Tsutsui, S. (2003). Advances in evolutionary computing: Theory and applications. New York: Springer.

    Google Scholar 

  • Gök, A. (2015). A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA. Measurement, 70, 100–109.

    Google Scholar 

  • Gök, A., Gök, K., Bilgin, M. B., & Alkan, M. A. (2017). Effects of cutting parameters and tool-path strategies on tool acceleration in ball-end milling. Materials and Technology, 51(6), 957–965.

    Google Scholar 

  • Gök, K., Gök, A., & Kisioglu, Y. (2015). Optimization of processing parameters of a developed new driller system for orthopedic surgery applications using Taguchi method. The International Journal of Advanced Manufacturing Technology, 76(5–8), 1437–1448.

    Google Scholar 

  • Gök, A., Gologlu, C., & Demirci, H. I. (2013). Cutting parameters and tool path style effects on cutting force and tool deflection in machining of convex and concave inclined surfaces. The International Journal of Advanced Manufacturing Technology, 69(5–8), 1063–1078.

    Google Scholar 

  • Graff, M., & Pe, R. (2013). Wind speed forecasting using genetic programming. In IEEE congress on evolutionary computation (pp. 408–415).

  • Griffin, J. (2014). The prediction of profile deviations when Creep Feed grinding complex geometrical features by use of neural networks and genetic programming with real-time simulation. The International Journal of Advanced Manufacturing Technology, 74(1–4), 1–16.

    Google Scholar 

  • Griffin, J., & Chen, X. (2009). Multiple classification of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming. The International Journal of Advanced Manufacturing Technology, 45, 1152–1168.

    Google Scholar 

  • Kahhal, P., Brooghani, S. Y. A., & Azodi, H. D. (2013). Multi-objective optimization of sheet metal forming die using FEA coupled with RSM. Journal of Mechanical Science and Technology, 27(12), 3835–3842.

    Google Scholar 

  • Karen, I., Kaya, N., & Ozturk, F. (2015). Intelligent die design optimization using enhanced differential evolution and response surface methodology. Journal of Intelligent Manufacturing, 26(5), 1027–1038.

    Google Scholar 

  • Kök, M., Kanka, E., & Eyercioglu, O. (2011). Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming. The International Journal of Advanced Manufacturing Technology, 55(9–12), 955–968.

    Google Scholar 

  • Kong, X., Lan, L., Hu, Z., Li, B., & Sui, T. (2015). Optimization of mechanical properties of high strength bainitic steel using thermos-mechanical control and accelerated cooling process. Journal of Materials Processing Technology, 217, 202–210.

    Google Scholar 

  • Korhonen, P., & Wallenius, J. (2008). Visualization in the multiple objective decision-making framework. In J. Branke, K. Deb, K. Miettinen, & R. Słowiński (Eds.), Multi-objective optimization (pp. 195–212). New York: Springer.

    Google Scholar 

  • Koza, J. (1992). Genetic programming: On the programming of computers by means of natural evolution. Cambridge: MIT Press.

    Google Scholar 

  • Kumar, H., Deb, D., & Chakravarty, D. (2017). Design of crown pillar thickness using finite element method and multivariate regression analysis. International Journal of Mining Science and Technology, 27(6), 955–964.

    Google Scholar 

  • Lagarias, J. F., Reeds, J. A., Wright, M. H., & Wright, P. E. (1998). Convergence properties of the Nelder–Mead Simplex method in low dimensions. Society for Industrial and Applied Mathematics, 9(1), 112–147.

    Google Scholar 

  • Lawson, C. L., & Hanson, R. J. (1995). Solving least squares problems. Philadelphia: Society for Industrial and Applied Mathematics.

    Google Scholar 

  • Lee, D. G., Lee, B. W., & Chang, S. H. (1997). Genetic programming model for long-term forecasting of electric power demand. Electric Power Systems Research, 40(1), 17–22.

    Google Scholar 

  • Lee, Y. S., & Tong, L. I. (2011). Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52(1), 147–152.

    Google Scholar 

  • Li, T. S., Huang, C. L., & Wu, Z. Y. (2006). Data mining using genetic programming for construction of a semiconductor manufacturing yield rate prediction system. Journal of Intelligent Manufacturing, 17(3), 355–361.

    Google Scholar 

  • Lian, H., Christiansen, A. N., Tortorelli, D. A., & Sigmund, O. (2017). Combined shape and topology optimization for minimization of maximal von Mises stress. Structural and Multidisciplinary Optimization, 55(5), 1541–1557.

    Google Scholar 

  • Lopes-Pereira, E., Luis de Oliveira, A., & Gomes-Fineza, A. (2017). Optimization of mechanical properties in concrete reinforced with fibers from solid urban wastes (PET bottles) for the production of ecological concrete. Construction and Building Materials, 149, 837–848.

    Google Scholar 

  • Lostado, R., Villanueva-Roldan, P., Fernandez-Martinez, R., & Mac Donald, B. J. (2016). Design and optimization of an electromagnetic servo braking system combining finite element analysis and weight-based multi-objective genetic algorithms. Journal of Mechanical Science and Technology, 30(8), 3591–3605.

    Google Scholar 

  • Lostado-Lorza, R., Escribano-Garcia, R., Fernandez-Martinez, R., Illera-Cueva, M., & Mac Donald, B. J. (2017). Using the finite element method and data mining techniques as an alternative to determine the maximum load capacity in tapered roller bearing. Journal of Applied Logic, 24(A), 4–14.

    Google Scholar 

  • Mansouri, I., Shariati, M., Safa, M., Ibrahim, Z., Tahir, M. M., & Petkovic, D. (2017). Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1306-6.

    Google Scholar 

  • Martin, A., & Schütze, O. (2014). A new predictor corrector variant for uncontrained bi-objective optimization problems. In EVOLVE—A bridge between probability, set oriented numerics, and evolutionay computation V (pp. 165–179).

  • McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239–245.

    Google Scholar 

  • Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308–313.

    Google Scholar 

  • Onwubolu, G. C., & Rayegani, F. (2014). Characterization and optimization of mechanical properties of ABS parts manufactured by the fused deposition modelling process. International Journal of Manufacturing Engineering. https://doi.org/10.1155/2014/598531.

    Google Scholar 

  • Panda, B., Shankhwar, K., Garg, A., & Savalani, M. M. (2016). Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1282-2.

    Google Scholar 

  • Pareto, V. (1906). Manuale di economia politica. New York: Macmillan.

    Google Scholar 

  • Perez, N., Cuate, O., Schütze, O., & Alvarado-Iniesta, A. (2016). Integración de las preferencias de los usuarios en el proceso de toma de decisiones para problemas de optimización de muchos objetivos discretos. Computación y Sistemas, 20(4), 589–607.

    Google Scholar 

  • Poli, R., Langdon, W. B., & McPhee, N. F. (2008). A field guide to genetic programming. San Francisco: Lulu Enterprises.

    Google Scholar 

  • Raju, M., Gupta, M. K., Bhanot, N., & Sharma, V. S. (2018). A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1420-0.

    Google Scholar 

  • Ren, W. X., & Chen, H. B. (2010). Finite element model updating in structural dynamics by using the response surface method. Engineering Structures, 32(8), 2455–2465.

    Google Scholar 

  • Sami-Ashhab, M., Breitsprecher, T., & Wartzack, S. (2014). Neural network based modeling and optimization of deep drawing-extrusion combined process. Journal of Intelligent Manufacturing, 25(1), 77–84.

    Google Scholar 

  • Schütze, O., Martin, A., Lara, A., Alvarado, S., Salinas, E., & Coello, C. A. (2016). The directed search method for multi-objective memetic algorithms. Computational Optimization and Applications, 63(2), 305–332.

    Google Scholar 

  • Shabani, M. O., & Mazahery, A. (2011). The ANN application in FEM modeling of mechanical properties of Al–Si allow. Applied Mathematical Modelling, 35(12), 5707–5713.

    Google Scholar 

  • Silva, S., & Almeida, J. (2003). GPLAB—A genetic programming toolbox for Matlab. In L. Gregersen (Ed.), Proceedings of the Nordic MATLAB conference (pp. 273–278).

  • Siva-Kumar, K., & Paulraj, G. (2014). Analysis and optimization of fixture under dynamic machining condition with chip removal effect. Journal of Intelligent Manufacturing, 25(1), 85–98.

    Google Scholar 

  • Smith, K. (1918). On the standard deviations of adjusted and interpolated values of an observed polynomial function and its constants and the guidance they give towards a proper choice of the distribution of observations. Biometrika, 12(1/2), 1–85.

    Google Scholar 

  • Toghroli, A., Suhatril, M., Ibrahim, Z., Safa, M., Shariati, M., & Shamshirband, S. (2016). Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1217-y.

    Google Scholar 

  • Umbrello, D., Ambrogio, G., Filice, L., Guerriero, F., & Guido, R. (2010). A clustering approach for determining the optimal process parameters in cutting. Journal of Intelligent Manufacturing, 21(6), 787–795.

    Google Scholar 

  • Umer, U., Qudeiri, J. A., Abdalmoneam, H., Hussein, M., Khan, A. A., & Al-Ahmari, A. R. (2014). Multi-objective optimization of oblique turning operations using finite element method and genetic algorithm. The International Journal of Advanced Manufacturing Technology, 71(1–4), 593–603.

    Google Scholar 

  • Yuan, J. Y. (1996). Numerical methods for generalized least squares problems. Journal of Computational and Applied Mathematics, 66(1–2), 571–584.

    Google Scholar 

  • Zhang, Q., & Li, H. (2007). MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.

    Google Scholar 

  • Zhou, Q., Cao, L., Zhou, H., & Xiang, H. (2018). Prediction of angular distortion in the fiber laser keyhole welding process based on a variable-fidelity approximation modeling approach. Journal of Intelligent Manufacturing, 29(3), 719–736.

    Google Scholar 

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  1. Department of Industrial and Manufacturing Engineering, Universidad Autónoma de Ciudad Juárez, Ave. Del Charro 450 Norte, 32315, Ciudad Juárez, Chihuahua, Mexico

    Alejandro Alvarado-Iniesta, Luis Gonzalo Guillen-Anaya, Luis Alberto Rodríguez-Picón & Raul Ñeco-Caberta

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  1. Alejandro Alvarado-Iniesta
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  2. Luis Gonzalo Guillen-Anaya
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  3. Luis Alberto Rodríguez-Picón
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Correspondence to Alejandro Alvarado-Iniesta.

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Alvarado-Iniesta, A., Guillen-Anaya, L.G., Rodríguez-Picón, L.A. et al. Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach. J Intell Manuf 31, 19–32 (2020). https://doi.org/10.1007/s10845-018-1432-9

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