Abstract
Real-world engineering optimisation problems are typically multi-objective and highly constrained, and constraints may be both costly to evaluate and binary in nature. In addition, objective functions may be computationally expensive and, in the commercial design cycle, there is a premium placed on rapid initial progress in the optimisation run. In these circumstances, evolutionary algorithms may not be the best choice; we have developed a multi-objective Tabu Search algorithm, designed to perform well under these conditions. Here we present the algorithm along with the constraint handling approach, and test it on a number of benchmark constrained test problems. In addition, we perform a parametric study on a variety of unconstrained test problems in order to determine the optimal parameter settings. Our algorithm performs well compared to a leading multi-objective Genetic Algorithm, and we find that its performance is robust to parameter settings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Harvey, S.: The Design Optimisation of Turbomachinery Blade Rows. PhD thesis, Cambridge University Engineering Department (2002)
Kellar, W.: Geometry Modelling in Compuational Fluid Dynamics and Design Optimisation. PhD thesis, Cambridge University Engineering Department (2002)
Gaiddon, A., Greard, J., Pagan, D., Knight, D.: Automated optimization of supersonic missile performance taking into account design uncertainties. Technical Report AIAA-2003-3879, AIAA (2003)
Duvigneau, R., Visonneau, M.: Hybrid genetic algorithms and artificial neural networks for compex design optimization in CFD. International Journal for Numerical Methods in Fluids 44, 1257–1278 (2004)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Winchester (2001)
Suppapitnarm, A., Seffen, K.A., Parks, G.T., Clarkson, J.: A simulated annealing algorithm for multiobjective optimization. Engineering Optimization 33, 59–85 (2000)
Jones, D., Mirrazavi, S., Tamiz, M.: Multi-objective meta-heuristics: An overview of the current state-of-the-art. European Journal of Operational Research 137, 1–9 (2002)
Hansen, M.: Tabu search for multiobjective optimization: MOTS. In: MCDM, Cape Town, South Africa (1997)
Baykasoglu, A., Owen, S., Gindy, N.: A taboo search based approach to find the pareto optimal set in multiple objective optimization. Engineering Optimization 31, 731–748 (1999)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)
Connor, A., Tilley, D.: A tabu search method for the optimisation of fluid power circuits. IMechE Journal of Systems and Control 212, 373–381 (1998)
Hooke, R., Jeeves, T.: Direct search solution of numerical and statistical problems. Journal of the ACM 8, 212–229 (1961)
Jameson, A.: A perspective on computational algorithms for aerodynamic analysis and design. Progress in Aerospace Sciences 37, 197–243 (2001)
Jaeggi, D., Asselin-Miller, C., Parks, G., Kipouros, T., Bell, T., Clarkson, P.: Multi-objective parallel tabu search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004, vol. 3242, pp. 732–741. Springer, Heidelberg (2004)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Kipouros, T., Parks, G., Savill, A., Jaeggi, D.: Multi-objective aerodynamic design optimisation. In: ERCOFTAC Design Optimisation: Methods and Applications Conference Proceedings, On CDRom. Paper ERCODO2004_239 (2004)
Kipouros, T., Jaeggi, D., Dawes, W., Parks, G., Savill, A.: Multi-objective optimisation of turbomachinery blades using tabu search. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 897–910. Springer, Heidelberg (2005)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation 7, 117–132 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jaeggi, D., Parks, G., Kipouros, T., Clarkson, J. (2005). A Multi-objective Tabu Search Algorithm for Constrained Optimisation Problems. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-31880-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24983-2
Online ISBN: 978-3-540-31880-4
eBook Packages: Computer ScienceComputer Science (R0)