Abstract
This chapter deals with the fundamentals of the optimization. The concepts of stochastic optimization and how the stochastic optimization is advantageous over the deterministic approaches are described in Sect. 3.2. Heuristic and meta-heuristic optimization techniques are defined in Sect. 3.3, and it also presents various existing heuristic and meta-heuristic optimization techniques. The fundamentals of the swarm intelligence are given in Sect. 3.4. The applications of the swarm intelligence in various fields are also presented in this section.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Banzhaf W, Nordin P, Keller R, Francone F (1998) Genetic programming—An Introduction. San Francisco, CA: Morgan Kaufmann. ISBN 978-1558605107
Battiti R, Mauro B, Franco M (2008) Reactive search and intelligent optimization. Springer Verlag. ISBN 978-0-387-09623-0
Beni G, Wang J (1989) Swarm intelligence in cellular robotic systems, proceed. NATO advanced workshop on robots and biological systems. Tuscany, Italy, pp 26–30
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. ISBN 0-19-513159-2
Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247
Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13(5):533–549. doi:10.1016/0305-0548(86)90048-1
Glover Fred (1989) Tabu Search—Part 1. ORSA J Comput 1(2):190–206. doi:10.1287/ijoc.1.3.190
Granville V, Krivanek M, Rasson J-P (1994) Simulated annealing: a proof of convergence. IEEE transactions on pattern analysis and machine intelligence, 16(6):652–656. doi:10.1109/34.295910
Hooke R, Jeeves TA (1961) Direct search solution of numerical and statistical problems. J Assoc Comput Mach (ACM) 8(2):212–229. doi:10.1145/321062.321069
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of IEEE international conference on neural networks. pp 1942–1948. doi:10.1109/ICNN.1995.488968
Kirkpatrick S, Gelatt Jr CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680. Bibcode: 1983 Sci…220..671K. doi:10.1126/science.220.4598.671
Lewis M, Anthony B, George A (1992) The Behavioral Self-Organization of Nanorobots Using Local Rules. Proceedings of the 1992 IEEE/RSJ international conference on intelligent robots and systems
Millonas M (1994) Swarms, phase transitions, and collective intelligence. In: Artificial Life III, Addison-Wesley
Minoux, M. (1986). Mathematical programming: Theory and algorithms. Egon Balas foreword (Translated by Steven Vajda from the (1983 Paris: Dunod) French ed.). Chichester: A Wiley-Interscience Publication. John Wiley & Sons, Ltd. pp xxviii+489. ISBN 0-471-90170-9. MR 2571910. (2008 Second ed., in French: Programmation mathématique: Théorie et algorithmes. Editions Tec & Doc, Paris, 2008).pp xxx+711. ISBN 978-2-7430-1000-3
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program (report 826)
Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313. doi:10.1093/comjnl/7.4.308
Nocedal J, Wright S (1999) Numerical optimization, Springer Business Media
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. doi:10.1016/j.ins.2009.03.004
Russell Stuart J, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall, Upper Saddle River, New Jersey, pp 111–114. ISBN: 0-13-790395-2
Spall JC (2003) Introduction to stochastic search and optimization. Wiley. ISBN 0-471-33052-3
Storn R (1996) On the usage of differential evolution for function optimization. Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp 519–523
Underwood P (1983) Dynamic relaxation, in computational methods for transient analysis. In: Belytschko T, Hughes TJR (eds) New-Holland. Amsterdam, pp 245–265
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 The Author(s)
About this chapter
Cite this chapter
Kunche, P., Reddy, K.V.V.S. (2016). Heuristic and Meta-Heuristic Optimization. In: Metaheuristic Applications to Speech Enhancement. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-31683-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-31683-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31681-9
Online ISBN: 978-3-319-31683-3
eBook Packages: EngineeringEngineering (R0)