A hybrid ant colony for multiresponse mixed-integer problems

• Autorzy: Kushwaha S. , Mukherjee I.
• Języki publikacji: EN

Abstrakty

EN

In this paper, a hybrid ant colony optimization (ACO) is used to solve a multiple response optimization problem with mixed-integer (MI) search space. The work reported in this paper may be clasified into three part. The first part discusses on relevant litratures and the methodology to solve multiple response optimization problem. The second part provide details on the working principal, parameter tuning of a hybrid ACO proposed for mixed integer state space. In the hybrid ACO, genetic algorithm (GA) is used for intensification of the search strategy. Standard single response (objective) test functions are selected to verify the suitability of hybrid ACO. The third part of this research work illustrates the application of the hybrid ACO in a multiple response optimization (MRO) problem. Statistical experimentation, partial least square regression, 'maximin' desirability function, and hybrid ACO is used to solve the MRO problem. The results confirm the suitability of the hybid ACO for a typical MI MRO problem.

Słowa kluczowe

EN

ant colony optimization(ACO); desirability functions; genetic algorithm (GA); multiple response optimization(MRO)

• Wydawca: Publishing House of Poznan University of Technology
• Czasopismo: Research in Logistics & Production
• Rocznik: 2012
• Tom: Vol. 2, No. 4
• Strony: 317--327
• Opis fizyczny: Bibliogr. 16

Twórcy

autor

Kushwaha S.

autor

Mukherjee I.

• Department of Mathematics, Indian Institute of Technology Bombay,

Bibliografia

• 1. Bera, S. and Mukherjee, I. (2012) "An Ellipsoidal Distance-based Search Strategy of Ants for Nonlinear Single and Multiple Response Optimization Problems", [in:] European Journal of Operations Research, vol. 223, pp. 321-332, 2012.
• 2. Deep, K., Singh, K. P., Kansal, M. L., & Mohan, C., (2009), "A real coded genetic algorithm for solving integer and mixed integer optimization problems", [in:] Applied Mathematics and Computation, Vol. 212, pp. 505-518.
• 3. Deep, K., & Thakur, M. (2007), "A new mutation operator for real coded genetic algorithms", [in:] Applied Mathematics and Computation, Vol. 193, pp. 211-230.
• 4. Derringer, G. , Suich, R., (1980), "Simultaneous optimization of several response variables", [in:] Journal of Quality Technology, Vol. 12, No.4, pp. 214-219
• 5. Dorigo, M.(1992), Optimization, Learning and Natural Algorithms (in Italian). Ph.D. thesis, ipartimento di Elettronica, Politecnico di Milano, Italy.
• 6. Harrington, E. C. Jr., (1965), "The desirability function", [in:] Industrial Quality Control, Vol. 21,No.l0, pp. 494-498.
• 7. Haupt, R. L., Haupt, S. E., (2004), Practical genetic algorithms. 2nd ed. New York: John Wiley & Sons, Inc.
• 8. Khuri, A. I., (1996), Ghosh, S., & Rao, C. R. (Eds.), "Multiresponse surface methodology", Handbook of Statistics: Design and Analysis of Experiments, 13, pp. 377-406, North Holland: Elsevier Science.
• 9. Kim, K., and Lin, D., (2000), "Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions", [in:] Applied Statistics: Journal of the Royal Statistical Society, Series C, Vol. 49, No. 3, pp. 311-325.
• 10. Kushwaha, S. and Mukherjee, I. (2012) A Hybrid GA-based Ant Colony Strategy for Continuous Correlated Multiple Response Optimization Problem, [in:] IEEE Colloquium on Humanities, Science & Engineering Research, Malaysia 2012, In Press.
• 11. Liao, T., (2010/2011), Improved ant colony optimization algorithms for continuous and mixed discrete-continuous optimization problems. Supervisors: Dorigo, M. and Stutzle, T: [in:] Report d'avancement de recherche Annee , IRDIA.
• 12. Lind, E. E., Goldin, J., & Hickman J. B., (1960), "Fitting yield and cost response surfaces", [in:] Chemical Engineering Progress, Vol. 56, No.l 1, pp. 62-68.
• 13.Molga, M., & Smutnicki, C., (2005), "Test functions for optimization needs", available [in:] http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf.
• 14. Mukherjee, I., (2007), Modeling and Optimization of Abrasive Metal Cutting Processes. PhD Thesis, Indian Institute of Technology (IIT) Kharagpur, India.
• 15. Rosipal, R., & Kramer, N. (2006), Overview and Recent Advances in Partial Least Squares. In: Saunders, C. et al. (Eds.), LNCS, Vol. 3940, pp. 34-51.
• 16. Socha, K., Dorigo, M., (2008), "Ant colony optimization for continuous domains", [in:] European Journal of Operations Research, Vol.185, pp. 1155-1173.