Abstract
In the last thirty years, a great interest has been devoted to metaheuristics. We can try to point out some of the steps that have marked the history of metaheuristics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Group Search Optimizer is based on the behavior of animals living in groups, where producers search to find food and scroungers search for joining opportunities.
- 2.
The term “island” is used descriptively rather than literally here. That is, an island is not just a segment of land surrounded by water, but any habitat that is geographically isolated from other habitats, including lakes and mountaintops. The theory of island biogeography has also been extended to peninsulas, bays, and other only partially isolated areas.
- 3.
The decomposition of the problem consists in determining an appropriate number of subcomponents and the role each will play. The mechanism for dividing the optimization problem f into n subproblems and treating them almost independently of one another depends strongly on the properties of the function f.
References
Aickelin, U., Bentley, P., Cayzer, S., Kim, J., Mcleod, J.: Danger theory: The link between AIS and IDS? In: J. Timmis, P. Bentley, E. Hart (eds.) Artificial Immune Systems, Lecture Notes in Computer Science, pp. 147–155. Springer (2003)
Aickelin, U., Cayzer, S.: The danger theory and its application to artificial immune systems. In: Proceedings of the 1st International Conference on Artificial Immune Systems, pp. 141–148 (2002)
Anspach, M., Varela, F.: Le systme immunitaire : un soi cognitif autonome. In: D. Andler (ed.) Introduction aux sciences cognitives, p. 514. Gallimard, Paris (1992)
Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press (1996)
Becerra, R.L., Coello, C.A.C.: A cultural algorithm with differential evolution to solve constrained optimization problems. In: IBERAMIA, pp. 881–890 (2004)
Brenner, M.P., Levitov, L.S., Budrene, E.O.: Physical mechanisms for chemotactic pattern formation by bacteria. Biophysical Journal 74(4), 1677–1693 (1998)
Brest, J., Maucec, M.: Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Computing: A Fusion of Foundations, Methodologies and Applications 15(11), 2157–2174 (2011)
de Castro, L.N.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London (2002)
de Castro, L.N., Von Zuben, F.J.: aiNet: An artificial immune network for data analysis. In: H.A. Abbass, R.A. Sarker, C.S. Newton (eds.) Data Mining: A Heuristic Approach, Chap. 12, pp. 231–259. Idea Group (2001)
de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)
Chakraborty, U.: Advances in Differential Evolution, 1st edn. Springer (2008)
Coelho, G.P., Zuben, F.V.: omni-aiNet: An immune-inspired approach for omni optimization. In: Proceedings of the 5th International Conference on Artificial Immune Systems, pp. 294–308. Springer (2006)
Coello Coello, C.A., Becerra, R.L.: Adding knowledge and efficient data structures to evolutionary programming: A cultural algorithm for constrained optimization. In: GECCO, pp. 201–209 (2002)
Darwin, C.: Origin of Species. Gramercy, New York (1995)
Das, S., Biswas, A., Dasgupta, S., Abraham, A.: Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications. In: A. Abraham, A.E. Hassanien, P. Siarry, A. Engelbrecht (eds.) Foundations of Computational Intelligence. Studies in Computational Intelligence, vol. 3, pp. 23–55. Springer, Berlin, Heidelberg (2009)
Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)
Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, New York (1998)
Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: Models and applications. Applied Soft Computing 11(2), 1574–1587 (2011)
de Castro, L.N., Zuben, F.J.V.: An evolutionary immune network for data clustering. In: Proceedings of the 6th Brazilian Symposium on Neural Networks, pp. 84–89. IEEE Computer Society Press (2000)
Dorigo, M.: Optimization, learning and natural Algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)
Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation, and machine learning. Phys. D 2(1–3), 187–204 (1986)
Feo, T.A., Resende, M.G.C.: A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8(2), 67–71 (1989)
Ficici, S.G.: Solution concepts in coevolutionary algorithms. Ph.D. thesis, Brandeis University, Waltham, MA (2004). AAI3127125
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the Symposium on Research in Security and Privacy, pp. 202–212 (1994)
Galeano, J.C., Veloza-Suan, A., González, F.A.: A comparative analysis of artificial immune network models. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO ’05, pp. 361–368. ACM, New York (2005)
Garrett, S.M.: How do we evaluate artificial immune systems? Evolutionary Computation 13(2), 145–177 (2005)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: Harmony search. Simulation 76(2), 60–68 (2001)
Glover, F.: Future paths for integer programming and links to artificial intelligence. Computers and Operations Research 13(5), 533–549 (1986)
Goh, C.K., Tan, K.C.: A competitive–cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation 13, 103–127 (2009)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edn. Studies in Computational Intelligence. Addison-Wesley Longman (1989)
Greensmith, J., Aickelin, U.: The deterministic dendritic cell algorithm. In: P.J. Bentley, D. Lee, S. Jung (eds.), Artificial Immune Systems. LNCS, vol. 5132, pp. 291–302. Springer (2008)
Greensmith, J., Aickelin, U., Cayzer, S.: Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: C. Jacob, M. Pilat, P. Bentley, J. Timmis (eds.), Artificial Immune Systems, LNCS, vol. 3627, pp. 153–167. Springer (2005)
Greensmith, J., Aickelin, U., Twycross, J.: Detecting danger: Applying a novel immunological concept to intrusion detection systems. In: Proceedings of the 6th International Conference on Adaptive Computing in Design and Manufacture (ACDM2004), Bristol, UK (2004)
Guo, Y., Cheng, J., Cao, Y., Lin, Y.: A novel multi-population cultural algorithm adopting knowledge migration. Soft Computing 15, 897–905 (2011)
Hansen, N., Ostermeier, A., Gawelczyk, A.: On the adaptation of arbitrary normal mutation distributions in evolution strategies: The generating set adaptation. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 57–64. Morgan Kaufmann, San Francisco (1995)
Hart, E., Bersini, H., Santos, F.: Structure versus function: A topological perspective on immune networks. Natural Computing 9, 603–624 (2010)
Hart, E., McEwan, C., Timmis, J., Hone, A.: Advances in artificial immune systems. Evolutionary Intelligence 4(2), 67–68 (2011)
Hart, E., Timmis, J.: Application areas of AIS: The past, the present and the future. Applied Soft Computing 8(1), 191–201 (2008)
He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence 20, 89–99 (2007)
He, S., Wu, Q., Saunders, J.: A novel group search optimizer inspired by animal behavioural ecology. In: Proceedings of 2006 IEEE Congress on Evolutionary Computation, pp. 16–21, Vancouver (2006)
Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42, 228–234 (1990)
Jerne, N.K.: Towards a network theory of the immune system. Annals of Immunology 125C(1–2), 373–389 (1973)
Ji, Z., Dasgupta, D.: Revisiting negative selection algorithms. Evolutionary Computation 15(2), 223–251 (2007)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kim, J., Greensmith, J., Twycross, J., Aickelin, U.: Malicious code execution detection and response immune system inspired by the danger theory. CoRR abs/1003.4142 (2010)
Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Kowaliw, T., Kharma, N.N., Jensen, C., Moghnieh, H., Yao, J.: Using competitive co-evolution to evolve better pattern recognisers. International Journal of Computational Intelligence and Applications 5(3), 305–320 (2005)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems), 1st edn. MIT Press (1992)
Lin, C., Qing, A., Feng, Q.: A comparative study of crossover in differential evolution. Journal of Heuristics 17(6), 675–703 (2011). doi:10.1007/s10732-010-9151-1
Lin, C.J., Chen, C.H., Lin, C.T.: A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C 39, 55–68 (2009)
Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft Computing 9, 448–462 (2005)
Liu, Y., Passino, K.: Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors. Journal of Optimization Theory and Applications 115, 603–628 (2002)
Luke, S., Wiegand, P.R.: When coevolutionary algorithms exhibit evolutionary dynamics. In: A.M. Barry (ed.) GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pp. 236–241. AAAI, New York (2002)
Ma, H.: An analysis of the equilibrium of migration models for biogeography-based optimization. Information Sciences 180(18), 3444–3464 (2010)
MacArthur, R., Wilson, E.: The Theory of Biogeography. Princeton University Press, Princeton, NJ (1967)
Matzinger, P.: Tolerance, danger, and the extended family. Annual Review of Immunology 12, 991–1045 (1994)
Mezura-Montes, E., Reyes-Sierra, M., Coello Coello, C.: Multi-objective optimization using differential evolution: A survey of the state-of-the-art. In: U. Chakraborty (ed.) Advances in Differential Evolution. Studies in Computational Intelligence, vol. 143, pp. 173–196. Springer, Berlin (2008)
Montgomery, J., Chen, S.: An analysis of the operation of differential evolution at high and low crossover rates. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010). doi:10.1109/CEC.2010.5586128
Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions. I. Binary parameters. In: Proceedings of the 4th International Conference on Parallel Problem Solving from Nature, PPSN IV, pp. 178–187. Springer, London (1996)
Neri, F., Tirronen, V.: Recent advances in differential evolution: A survey and experimental analysis. Artificial Intelligence Review 33, 61–106 (2010)
Nguyen, T., Yao, X.: Hybridizing cultural algorithms and local search. In: E. Corchado, H. Yin, V. Botti, C. Fyfe (eds.) Intelligent Data Engineering and Automated Learning, IDEAL 2006. Lecture Notes in Computer Science, vol. 4224, pp. 586–594. Springer, Berlin, (2006)
Ochoa, A., Ponce, J., Hernández, A., Li, L.: Resolution of a combinatorial problem using cultural algorithms. JCP 4(8), 738–741 (2009)
Paredis, J.: Steps towards co-evolutionary classification neural networks. In: R.A. Brooks, P. Maes (eds.) Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems (Artificial Life IV), pp. 102–108. Cambridge, MA (1994). http://www.mpi-sb.mpg.de/services/library/proceedings/contents/alife94.html
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine 22(3), 52–67 (2002). doi:10.1109/MCS.2002.1004010
Passino, K.M.: Bacterial foraging optimization. International Journal of Swarm Intelligence Research 1(1), 1–16 (2010)
Pollack, J.B., Blair, A.D.: Co-evolution in the successful learning of backgammon strategy. Machine Learning 32, 225–240 (1998)
Popovici, E., De Jong, K.: The effects of interaction frequency on the optimization performance of cooperative coevolution. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO ’06, pp. 353–360. ACM, New York (2006)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Potter, M.A., Jong, K.A.D.: A cooperative coevolutionary approach to function optimization. In: Proceedings of the International Conference on Evolutionary Computation, Third Conference on Parallel Problem Solving from Nature, PPSN III, pp. 249–257. Springer, London, (1994)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005)
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A gravitational search algorithm. Information Sciences 179(13), 2232–2248 (2009). doi:10.1016/j.ins.2009.03.004
Renfrew, A.: Dynamic modeling in archaeology: what, when, and where? In: Dynamical Modeling and the Study of Change in Archaelogy (1994)
Reynolds, R.G.: An adaptive computer model of plan collection and early agriculture in the eastern valley of Oaxaca. In: G. Naquitz (ed.) Archaic Foraging and Early Agriculture in Oaxaca, Mexico, pp. 439–500 (1986)
Reynolds, R.G.: An introduction to cultural algorithms. In: A.V. Sebalk, L.J. Fogel (eds.) Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131–139. World Scientific, River Edge, NJ (1994)
Reynolds, R.G.: Cultural algorithms: Theory and applications. In: D. Corne, M. Dorigo, F. Glover (eds.) New Ideas in Optimization, pp. 367–378. McGraw-Hill, Maidenhead, UK (1999)
Reynolds, R.G., Kohler, T.A., Kobti, Z.: The effects of generalized reciprocal exchange on the resilience of social networks: An example from the prehispanic Mesa Verde region. Computational and Mathematical Organization Theory 9, 227–254 (2003)
Reynolds, R.G., Liu, D.: Multi-objective cultural algorithms. In: IEEE Congress on Evolutionary Computation, pp. 1233–1241 (2011)
Reynolds, R.G., Peng, B., Ali, M.Z.: The role of culture in the emergence of decision-making roles: An example using cultural algorithms. Complexity 13(3), 27–42 (2008)
Rivera, D.C., Becerra, R.L., Coello Coello Carlos, A.: Cultural algorithms, an alternative heuristic to solve the job shop scheduling problem. Engineering Optimization 39(1), 69–85 (2007)
Rosin, C.D., Belew, R.K.: New methods for competitive coevolution. Evolutionary Computation 5, 1–29 (1997)
Rychtyckyj, N., Reynolds, R.G.: Using cultural algorithms to re-engineer large-scale semantic networks. International Journal of Software Engineering and Knowledge Engineering 15(4), 665–694 (2005)
Saleem, S.M.: Knowledge-based solution to dynamic optimization problems using cultural algorithms. Ph.D. thesis, Wayne State University, Detroit, MI (2001)
Secker, A., Freitas, A., Timmis, J.: A danger theory inspired approach to web mining. In: J. Timmis, P. Bentley, E. Hart (eds.) Artificial Immune Systems. Lecture Notes in Computer Science, vol. 2787, pp. 156–167. Springer, Berlin, Heidelberg (2003). doi:10.1007/978-3-540-45192-1_16
Shi, Y.J., Teng, H.F., Li, Z.Q.: Cooperative co-evolutionary differential evolution for function optimization. In: L. Wang, K. Chen, Y. Ong (eds.) Advances in Natural Computation, Lecture Notes in Computer Science, vol. 3611, pp. 428–428. Springer, Berlin, (2005)
Simon, D.: Evolutionary Optimization Algorithms: Biologically-Inspired and Population-Based Approaches to Computer Intelligence. p. 624. Wiley (2013)
Simon, D.: Biogeography-based optimization. IEEE Transactions on Evolutionary Computation 12(6), 702–713 (2008)
Sims, K.: Evolving 3D morphology and behavior by competition. Artificial Life 1(4), 353–372 (1994)
Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research 21(1), 63–100 (2004)
Storn, R.M., Price, K.V.: Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Talbi, E.G.: Metaheuristics: From Design to Implementation, 1st edn. Wiley-Blackwell (2009)
Tan, K.C., Yang, Y.J., Goh, C.K.: A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Transactions on Evolutionary Computation 10(5), 527–549 (2006)
Teng, N., Teo, J., Hijazi, M., Hanafi, A.: Self-adaptive population sizing for a tune-free differential evolution. Soft Computing 13, 709–724 (2009)
Timmis, J., Andrews, P., Hart, E.: On artificial immune systems and swarm intelligence. Swarm Intelligence 4(4), 247–273 (2010)
Timmis, J., Andrews, P., Owens, N., Clark, E.: An interdisciplinary perspective on artificial immune systems. Evolutionary Intelligence 1(1), 5–26 (2008)
Timmis, J., Hone, A., Stibor, T., Clark, E.: Theoretical advances in artificial immune systems. Theoretical Computer Science 403(1), 11–32 (2008)
Tylor, E.B.: Primitive Culture, vol. 2, 7th edition. Brentano’s, New York (1924)
Ulutas, B.H., Kulturel-Konak, S.: A review of clonal selection algorithm and its applications. Artificial Intelligence Review 36(2), 117–138 (2011)
Walker, A., Hallam, J., Willshaw, D.: Bee-havior in a mobile robot: The construction of a self-organized cognitive map and its use in robot navigation within a complex, natural environment. In: Proceedings of ICNN’93, International Conference on Neural Networks, vol. III, pp. 1451–1456. IEEE Press, Piscataway, NJ (1993)
Wallace, A.R.: The Geographical Distribution of Animals (two volumes). Adamant Media Corporation, Boston, MA (2005)
Weber, M.: Parallel global optimization, structuring populations in differential evolution. Ph.D. thesis, University of Jyvskyl (2010)
Wiegand, R.P.: An analysis of cooperative coevolutionary algorithms. Ph.D. thesis, George Mason University, Fairfax, VA (2004). AAI3108645
Wu, C., Zhang, N., Jiang, J., Yang, J., Liang, Y.: Improved bacterial foraging algorithms and their applications to job shop scheduling problems. In: Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, Part I, ICANNGA ’07, pp. 562–569. Springer, Berlin, Heidelberg (2007)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: J.R. González, D.A. Pelta, C. Cruz, G. Terrazas, N. Krasnogor (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284, Chap. 6, pp. 65–74. Springer, Berlin Heidelberg (2010)
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), Coimbatore, India, IEEE Conference Publications, pp. 210–214. IEEE Press, Piscataway, NJ (2009)
Zaharie, D.: Influence of crossover on the behavior of differential evolution algorithms. Applied Soft Computing 9(3), 1126–1138 (2009). doi:10.1016/j.asoc.2009.02.012
Zhang, C., Yi, Z.: A danger theory inspired artificial immune algorithm for on-line supervised two-class classification problem. Neurocomputing 73(7–9), 1244–1255 (2010). doi:10.1016/j.neucom.2010.01.005. http://www.sciencedirect.com/science/article/pii/S0925231210000573
Zheng, J., Chen, Y., Zhang, W.: A survey of artificial immune applications. Artificial Intelligence Review 34, 19–34 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Boussaïd, I. (2016). Some Other Metaheuristics. In: Siarry, P. (eds) Metaheuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-45403-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-45403-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45401-6
Online ISBN: 978-3-319-45403-0
eBook Packages: Computer ScienceComputer Science (R0)