Abstract
Turing’s pioneer work in heuristic search has inspired many generations of research in heuristic algorithms. In the last two decades, metaheuristic algorithms have attracted strong attention in scientific communities with significant developments, especially in areas concerning swarm intelligence based algorithms. In this work, we will briefly review some of the important achievements in metaheuristics, and we will also discuss key implications in applications and topics for further research.
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Keywords
- Differential Evolution
- Swarm Intelligence
- Harmony Search
- Metaheuristic Algorithm
- National Physical Laboratory
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Afshar, A., Haddad, O.B., Marino, M.A., Adams, B.J.: Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. J. Franklin Institute 344, 452–462 (2007)
Auger, A., Teytaud, O.: Continuous lunches are free plus the design of optimal optimization algorithms. Algorithmica 57, 121–146 (2010)
Auger, A., Doerr, B.: Theory of Randomized Search Heuristics: Foundations and Recent Developments. World Scientific (2010)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimisation: Overview and conceptural comparision. ACM Comput. Surv. 35, 268–308 (2003)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evolutionary Computation 6, 58–73 (2002)
Copeland, B.J.: The Essential Turing. Oxford University Press (2004)
Corne, D., Knowles, J.: Some multiobjective optimizers are better than others. In: Evolutionary Computation, CEC 2003, vol. 4, pp. 2506–2512 (2003)
Christensen, S., Oppacher, F.: Wath can we learn from No Free Lunch? In: Proc. Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 1219–1226 (2001)
Durgun, I., Yildiz, A.R.: Structural design optimization of vehicle components using cuckoo search algorithm. Materials Testing 3, 185–188 (2012)
Dorigo, M., Stütle, T.: Ant Colony Optimization. MIT Press (2004)
Floudas, C.A., Pardolos, P.M.: Encyclopedia of Optimization, 2nd edn. Springer (2009)
Geem, Z.W.: Music-Inspired Harmony Search Algorithm: Theory and Applications. Springer (2009)
Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a meteheuristic approach to solve structural optimization problems. In: Engineering with Computers, July 29 (2011), doi:10.1007/s00366-011-0241-y
Gandomi, A.H., Yang, X.S., Talatahari, S., Deb, S.: Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Computers & Mathematics with Applications 63(1), 191–200 (2012)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Addison-Wesley, Reading (2002)
Gutjahr, W.J.: Convergence Analysis of Metaheuristics. Annals of Information Systems 10, 159–187 (2010)
Holland, J.: Adaptation in Natural and Artificial systems. University of Michigan Press, Ann Anbor (1975)
Igel, C., Toussaint, M.: On classes of functions for which no free lunch results hold. Inform. Process. Lett. 86, 317–321 (2003)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Turkey (2005)
Kennedy, J., Eberhart, R.: Particle swarm optimisation. In: Proc. of the IEEE Int. Conf. on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Kirkpatrick, S., Gellat, C.D., Vecchi, M.P.: Optimisation by simulated annealing. Science 220, 671–680 (1983)
Nakrani, S., Tovey, C.: On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers. Adaptive Behaviour 12(3-4), 223–240 (2004)
Neumann, F., Witt, C.: Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Springer (2010)
Marshall, J.A., Hinton, T.G.: Beyond no free lunch: realistic algorithms for arbitrary problem classes. In: WCCI 2010 IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 18-23, pp. 1319–1324 (2010)
Parpinelli, R.S., Lopes, H.S.: New inspirations in swarm intelligence: a survey. Int. J. Bio-Inspired Computation 3, 1–16 (2011)
Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The Bees Algorithm A Novel Tool for Complex Optimisation Problems. In: Proceedings of IPROMS 2006 Conference, pp. 454–461 (2006)
Price, K., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer (2005)
Schumacher, C., Vose, M., Whitley, D.: The no free lunch and problem description length. In: Genetic and Evolutionary Computation Conference, GECCO 2001, pp. 565–570 (2001)
Shilane, D., Martikainen, J., Dudoit, S., Ovaska, S.J.: A general framework for statistical performance comparison of evolutionary computation algorithms. Information Sciences 178, 2870–2879 (2008)
Spall, J.C., Hill, S.D., Stark, D.R.: Theoretical framework for comparing several stochastic optimization algorithms. In: Probabilistic and Randomized Methods for Design Under Uncertainty, pp. 99–117. Springer, London (2006)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Turing, A.M.: Intelligent Machinery. Technical Report, National Physical Laboratory (1948)
Villalobos-Arias, M., Coello Coello, C.A., Hernández-Lerma, O.: Asymptotic convergence of metaheuristics for multiobjective optimization problems. Soft Computing 10, 1001–1005 (2005)
Walton, S., Hassan, O., Morgan, K., Brown, M.R.: Modified cuckoo search: a new gradient free optimization algorithm. Chaos, Solitons & Fractals 44(9), 710–718 (2011)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimisation. IEEE Transaction on Evolutionary Computation 1, 67–82 (1997)
Wolpert, D.H., Macready, W.G.: Coevolutonary free lunches. IEEE Trans. Evolutionary Computation 9, 721–735 (2005)
Turing Archive for the History of Computing, www.alanturing.net
Yang, X.-S.: Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 317–323. Springer, Heidelberg (2005)
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)
Yang, X.-S.: Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Computation 2, 78–84 (2010a)
Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. John Wiley and Sons, USA (2010b)
Yang, X.-S.: A New Metaheuristic Bat-Inspired Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N., et al. (eds.) NICSO 2010. Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Heidelberg (2010c)
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceeings of World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 210–214. IEEE Publications, USA (2009)
Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Modelling & Num. Optimisation 1, 330–343 (2010)
Yang, X.S.: Bat algorithm for multi-objective optimisation. Int. J. Bio-Inspired Computation 3(5), 267–274 (2011)
Yang, X.S., Deb, S.: Two-stage eagle strategy with differential evolution. Int. J. Bio-Inspired Computation 4(1), 1–5 (2012)
Yang, X.S., Hossein, S.S., Gandomi, A.H.: Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing 12(3), 1180–1186 (2012)
Yang, X.S., Deb, S.: Multiobjective cuckoo search for design optimization. Computers and Operations Research (October 2011) (accepted), doi:10.1016/j.cor.2011.09.026
Yu, L., Wang, S.Y., Lai, K.K., Nakamori, Y.: Time series forecasting with multiple candidate models: selecting or combining? Journal of Systems Science and Complexity 18(1), 1–18 (2005)
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Yang, XS. (2013). Metaheuristic Optimization: Nature-Inspired Algorithms and Applications. In: Yang, XS. (eds) Artificial Intelligence, Evolutionary Computing and Metaheuristics. Studies in Computational Intelligence, vol 427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29694-9_16
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DOI: https://doi.org/10.1007/978-3-642-29694-9_16
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