Multi-Objective Tabu Search Algorithm to Minimize Weight and Improve Formability of Al3105-St14 Bi-Layer Sheet
الموضوعات :M Ehsanifar 1 , H Momeni 2 , N Hamta 3 , A. R Nezamabadi 4
1 - Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran
2 - Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran
3 - Department of Mechanical Engineering, Arak University of Technology, Arak, Iran
4 - Department of Mechanical Engineering, Arak Branch, Islamic Azad University, Arak, Iran
الکلمات المفتاحية: Bi-layer metallic sheet, Pareto front, Forming limit diagram (FLD), Tabu Search Algorithm,
ملخص المقالة :
Nowadays, with extending applications of bi-layer metallic sheets in different industrial sectors, accurate specification of each layer is very prominent to achieve desired properties. In order to predict behavior of sheets under different forming modes and determining rupture limit and necking, the concept of Forming Limit Diagram (FLD) is used. Optimization problem with objective functions and important parameters aims to find optimal thickness for each of Al3105-St14 bi-layer metallic sheet contributors. Optimized point is achieved where formability of the sheet approaches to maximum extent and its weight to minimum extent. In this paper, multi-objective Tabu search algorithm is employed to optimize the considered problem. Finally, derived Pareto front using Tabu search algorithm is presented and results are compared with the solutions obtained from genetic algorithm. Comparison revealed that Tabu search algorithm provides better results than genetic algorithm in terms of Mean Ideal Distance, Spacing, non-uniformity of Pareto front and CPU time.
[1] Keeler S., Backhofen W., 1964, Plastic instability and fracture in sheet stretched over rigid punches, ASM Transactions Quarterly 56: 25-48.
[2] Goodwin G.M., 1968, Application of strain analysis to sheet metal forming in the press shop, SAE Paper 680093(1): 380-387.
[3] Semiatin S.L., Piehler H.R., 1979, Deformation of sandwich sheet materials in uniaxial tension, Metallurgical Transactions A 10(1): 85-96.
[4] Semiatin S.L., Piehler H.R., 1979, Formability of sandwich sheet materials in plane strain compression and rolling, Metallurgical Transactions A 10(1): 97-107.
[5] Darabi R., Deilami Azodi H., Bagherzadeh S., 2017, Investigation into the effect of material properties and arrangement of each layer on the formability of bimetallic sheets, Journal of Manufacturing Processes 29: 133-148.
[6] Deilami Azodi H., Safari M., Darabi R., 2017, Formability prediction of two-layer sheets based on ductile fracture criteria, Transactions of the Indian Institute of Metals 70(7): 1841-1847.
[7] Chang T.J., Meade N., Beasley J.E., Sharaiha Y.M., 2000, Heuristics for cardinality constrained portfolio optimisation, Computers and Operations Research 27(13): 1271-1302.
[8] Gendreau M., Potvin J.Y., 2010, Handbook of Metaheuristics, Springer US.
[9] Ma C., Yang Y., Wang L., Chu C., Ma C., An L., 2019, Research on distribution route with time window and on-board constraint based on tabu search algorithm, Eurasip Journal on Wireless Communications and Networking 2019(1): 25.
[10] Glover F., 1977, Heuristics for integer programming using surrogate constraints, Decision Sciences 8(1): 156-166.
[11] Glover F., 1986, Future paths for integer programming and links to artificial intelligence, Computers and Operations Research 13(5): 533-549.
[12] Glover F., 2006, Parametric Tabu-search for mixed integer programs, Computers and Operations Research 33(9): 2449-2494.
[13] Jaeggi D.M., Parks G.T., Kipouros T., Clarkson P. J., 2008, The development of a multi-objective Tabu Search algorithm for continuous optimisation problems, European Journal of Operational Research 185(3): 1192-1212.
[14] Holland J.H., 1975, Adaptation in Natural and Artificial Systems, The University of Michigan.
[15] Schaffer J.D., 1985, Multiple objective optimization with vector evaluated genetic algorithms, The 1st International Conference on Genetic Algorithms.
[16] Knowles J.D., Corne D.W., 2000, Approximating the nondominated front using the Pareto archived evolution strategy, Evolutionary Computation 8(2): 149-172.
[17] Corne D.W., Jerram N.R., Knowles J.D., 2001, PESA-II: Region-based selection in evolutionary multi objective optimization, Proceedings of the 3rd Annual Genetic and Evolutionary Computation.
[18] Sarker R., Liang K., Newton C., 2002, A new multiobjective evolutionary algorithm, European Journal of Operational Research 140(1): 12-23.
[19] Yen G.G., Lu H., 2003, Dynamic multiobjective evolutionary algorithm: Adaptive cell-based rank and density estimation, IEEE Transactions on Evolutionary Computation 7(3): 253-274.
[20] Darabi R., Deilami Azodi H., Jung D.W., 2018, Multi-objective optimization of bi-layer metallic sheet using Pareto-based genetic algorithm, Materials Science Forum 917: 276-283.
[21] Branke J., Deb K., Miettinen K., Slowinski R., 2008, Multiobjective Optimization: Interactive and Evolutionary Approaches, Germany,Springer.
[22] 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.
[23] Raquel C.R., Naval Prospero J., 2005, An effective use of crowding distance in multiobjective particle swarm optimization, Proceedings of the 2005 Conference on Genetic and Evolutionary Computation.
[24] Liu D., Tan K. C., Goh C. K., Ho W. K., 2007, A multiobjective memetic algorithm based on particle swarm optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 37(1): 42-50.
[25] Arjmand M., Najafi A. A., 2015, Solving a multi-mode bi-objective resource investment problem using meta-heuristic algorithms, Advanced Computational Techniques in Electromagnetics 2015(1): 41-58.
[26] Sun G., Li G., Gong Z., Cui X., Yang X., Li Q., 2010, Multiobjective robust optimization method for drawbead design in sheet metal forming, Materials & Design 31(4): 1917-1929.