Abstract
This chapter presents critical survey of methods, approaches and tendencies observed in modern optimization, focusing on nature-inspired techniques recommended for particularly hard discrete problems generated by practice. Applicability of these methods, depending the class of stated optimization task and classes of goal function, have been discussed. The best promising approaches have been indicated with practical recommendation of using. Some numerical as well as theoretical properties of these algorithms are also shown.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aarts, E.H.L., van Laarhoven, P.J.M.: Simulated Annealing: a Pedestrian Review of the Theory and Some Applications. In: Deviijver, P.A., Kittler, J. (eds.) Pattern Recognition and Applications. Springer, Heidelberg (1987)
Aarts, E.H.L., Lenstra, J.K.: Local Search in Combinatorial Optimization. Princeton University Press, Princeton (2003)
Abdul-Razaq, T.S., Potts, C.N., Van Wassenhove, L.N.: A Survey of Algorithms for the Single Machine Total Weighted Tardiness Scheduling Problem. Discrete Applied Mathematics 26, 235–253 (1990)
Adleman, L.M.: Molecular Computation of Solutions to Combinatorial Problems. Science 266, 1021–1024 (1994)
Alba, E.: Parallel Metaheuristics: a New Class of Algorithms. John Wiley & Sons, Chichester (2005)
Albers, S.: On-Line Algorithms: a Survey. Mathematical Programming 97, 3–24 (2003)
Amin, S.: Simulated Jumping. Annals of Operations Research 86, 23–38 (1999)
Angel, E., Zissimopoulos, V.: On the Landscape Ruggedness of the Quadratic Assignment Problem. Theoretical Computer Science 263, 159–172 (2001)
Balas, E., Vazacopoulos, A.: Guided Local Search with Shifting Bottleneck for Job-Shop Scheduling. Management Science 44, 262–275 (1998)
Bartak, R.: On-line guide to Constraint programming (2010), http://ktiml.mff.cuni.cz/bartak/constraints/
Corne, D., Dorigo, M., Glover, F.: New Ideas in Optimization. McGraw Hill, Cambridge (1999)
Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Tansactions on Systems, Man, and Cybernetics: Part B 26, 29–41 (1996)
Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Books (2004)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H.Freeman and Co., New York (1979)
Glover, F.: Tabu Search and Adaptive Memory Programing - Advances, Application and Challenges. In: Barr, R.S., Helgason, R.V., Kennington, J.L. (eds.) Interfaces in Computer Science and Operations Research, Kluwer, Dordrecht (1996)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Haupt, R.: A Survey of Priority Rule-Based Scheduling. OR Spectrum 11, 3–16 (1989)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, MI (1975)
Karaboga, D., Basturk, B.: A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization 39, 459–471 (2007)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE International Conference on Neural Networks (Perth, Australia), vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway (1942)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing, Science. Science 220, 671–680 (1983)
Merz, P., Freisleben, B.: Fitness Landscapes and Memetic Algorithms Design. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization. McGraw-Hill, New York (1999)
Nowicki, E., Smutnicki, C.: A Fast Taboo Search Algorithm for the Job Shop Problem. Management Science 42, 797–813 (1996)
Nowicki, E., Smutnicki, C.: An Advanced Tabu Search Algorithm for the Job Shop Problem. Journal of Scheduling 8, 145–159 (2005)
Nowicki, E., Smutnicki, C.: Some Aspects of Scatter Search in the Flow-Shop Problem. European Journal of Operational Research 169, 654–666 (2006)
Nowicki, E., Smutnicki, C.: Some New Ideas in TS for Job Shop Scheduling. In: Rego, C., Alidaee, B. (eds.) Metaheuristic Optimization via Memory and Evolution. Tabu Search and Scatter Search, pp. 165–190. Kluwer, Dordrecht (2005)
Panwalker, S.S., Iskander, W.: A Survey of Scheduling Rules. Operations Research 25, 45–61 (1977)
Pinedo, M.: Scheduling: Theory, Algorithms, and Systems. Springer, Heidelberg (2008)
Reeves, C., Yamada, T.: Genetic Algorithms, Path Relinking, and the Flowshop Sequencing Problem. Evolutionary Computation 6, 45–60 (1998)
Schumer, M., Steiglitz, K.: Adaptive Step Size Random Search. IEEE Transactions on Automatic Control 13, 270–276 (1968)
Sevast’janov, S.V.: On some geometric methods in scheduling theory: a survey. Discrete Applied Mathematics 55, 59–82 (1994)
Smutnicki, C.: Optimization and Control in JIT Manufacturing Systems. Oficyna Wydawnicza PWr, Wroclaw (1997)
Wenzel, W., Hamacher, K.: A Stochastic Tunneling Approach for Global Minimization of Complex Potential Energy Landscapes. Physical Review Letters 82, 3003 (1999)
Weinberger, E.D.: Correlated and Uncorrelated Fitness Landscapes and How to Tell the Difference. Biological Cybernetics 63, 325–336 (1990)
Werner, F., Winkler, A.: Insertion Techniques for the Heuristic Solution of the Job Shop Problem. Discrete Applied Mathematics 58, 191–211 (1995)
Wierzchon, S.T.: Artificial Immune Systems. Theory and application. EXIT, Warsaw (2001) (Polish)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)
Zhou, D., Cherkassky, V., Baldwin, T.R., Olson, D.E.: A Neural Network Approach to Job-shop Scheduling. IEEE Transactions on Neural Networks 2, 175–179 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Smutnicki, C. (2012). Optimization Technologies for Hard Problems. In: Fodor, J., Klempous, R., Suárez Araujo, C.P. (eds) Recent Advances in Intelligent Engineering Systems. Studies in Computational Intelligence, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23229-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-23229-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23228-2
Online ISBN: 978-3-642-23229-9
eBook Packages: EngineeringEngineering (R0)