Abstract
Many problems can be formulated as optimization problems. Among the many classes of algorithms for solving such problems, one interesting, biologically inspired group is that of meta-heuristic optimization techniques. In this introductionary chapter we provide an overview of such techniques, in particular of Genetic Algorithms, Ant Colony Optimization and Particle Swarm Optimization techniques.
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References
Bullheimer, B., Hartl, R.F., Strauss, C.: A new rank-based version of the Ant System: A computational study. Central European Journal for Operations Research and Economics 7(1), 25–38 (1999)
Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD Thesis, Politecnico di Milano, Italy (1992)
Di Caro, G., Dorigo, M.: AntNet: Distributed Stigmergetic Control for Communications Networks. Journal of Artificial Intelligence Research 9, 317–365 (1998)
Dorigo, M., Di Caro, G.D., Gambardella, L.M.: Ant Algorithms for Discrete Optimization. Artificial Life 5, 137–172 (1999)
Dorigo, M., Maniezzo, V., Colorni, A.: Positive Feedback as a Search Strategy, Technical Report No 91-016, Politecnico di Milano (1991)
Dorigo, M., Gambardella, L.: Ant Colony System: A Cooperative Learning Approach to the Travelling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Edelbaum, T.N.: Theory of Maxima and Minima. In: Leitmann (ed.) Optimization Techniques with Applications to Aerospace Systems, p. 132. Academic Press, New York (1962)
Fenton, N., Hill, G.: Systems construction and analysis: a mathematical and logical framework. McGraw-Hill, New York (1993)
Fonseca, C.M., Fleming, P.J.: An Overview of Evolutional Algorithms in Multiobjective Optimization. Evolutionary Computation (31), 1–16 (1995)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Gaertner, D., Clark, K.: On Optimal Parameters for Ant Colony Optimization algorithms. In: Proceedings of the International Conference on Artificial Intelligence 2005, Las Vegas, USA, pp. 83–89 (2005)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Goss, S., Aron, S., Deneubourg, J.L., Pasteels, J.M.: Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76, 579–581 (1989)
Gutjahr, W.: A graph-based ant system and its convergence. Future Generation Computer Systems 16(8), 873–888 (2000)
Kennedy, J., Eberhart, E.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE Press, Los Alamitos (1995)
Michel, R., Middendorf, M.: An Island Model Based Ant System with Lookahead for the Shortest Supersequence Problem. In: Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature, Amsterdam, The Netherlands, pp. 692–701 (1998)
Shmygelska, A., Hoos, H.H.: An Ant Colony Optimisation Algorithm for the 2D and 3D Hydrophobic Polar Protein Folding Problem. BMC Bioinformatics 6, 6–30 (2005)
Stützle, T., Dorigo, M.: A short convergence proof for a class of ant colony optimisation algorithms. IEEE Transactions on Evolutionary Computation 6(4), 358–365 (2002)
Stützle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)
Schwefel, H.-P.: Evolution and Optimum Seeking. John Wiley & Son, Inc., New York (1995)
Syswerda, G.: Uniform Crossover in Genetic Algorithms. In: Proceedings of International Conference on Genetic Algorithm 1989 (ICGA 1989), pp. 2–9 (1989)
Wright, S.: Evolution in Mendelian populations. Genetics 16, 97–159 (1931)
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Nolle, L., Köppen, M., Schaefer, G., Abraham, A. (2011). Intelligent Computational Optimization in Engineering: Techniques and Applications. In: Köppen, M., Schaefer, G., Abraham, A. (eds) Intelligent Computational Optimization in Engineering. Studies in Computational Intelligence, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_1
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DOI: https://doi.org/10.1007/978-3-642-21705-0_1
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