Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2015 | OriginalPaper | Buchkapitel

1. Brief History and Overview of Intelligent Optimization Algorithms

verfasst von : Fei Tao, Yuanjun Laili, Lin Zhang

Erschienen in: Configurable Intelligent Optimization Algorithm

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Up to now, intelligent optimization algorithm has been developed for nearly 40 years. It is one of the main research directions in the field of algorithm and artificial intelligence. No matter for complex continuous problems or discrete NP-hard combinatorial optimizations, people nowadays is more likely to find a feasible solution by using such randomized iterative algorithm within a short period of time instead of traditional deterministic algorithms. In this chapter, the basic principle of algorithms, research classifications, and the development trends of intelligent optimization algorithm are elaborated.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Nächstes Kapitel Recent Advances of Intelligent Optimization Algorithm in Manufacturing
Literatur
1.
Nocedal J, Wright SJ (2006) Numerical optimization. Springer, Berlin
2.
Bonnans JF, Gilbert JC, Lemarechal C, Sagastizabal CA (2006) Numerical optimization: theoretical and practical aspects. Springer, Berlin
3.
Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Dover Publications, Mineola
4.
Schrijver A (2003) Combinatorial optimization. Springer, Berlin
5.
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. J ACM Comput Surv (CSUR) 35(3)68–308
6.
Garey MR, Johnson DS (1990) Computers and intractability: a guide to the theory of NP-completeness. W. H Freeman and Co, San Francisco
7.
8.
Gawiejnowics S (2008) Time-dependent scheduling. Springer, Berlin
9.
10.
Kann V (1992) On the approximability of NP-complete optimization problems. Royal Institute of Technology, Sweden
11.
Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, New york
12.
Ribeiro CC, Martins SL, Rosseti I (2007) Metaheuristics for optimization problems in computer communications. Comput Commun 30(4):656–669CrossRef
13.
Liao TW, Egbelu PJ, Sarker BR, Leu SS (2011) Metaheuristics for project and construction management—a state-of-the-art review. Autom Constr 20(5):491–505CrossRef
14.
Moscato P (1989) On evolution, Search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program
15.
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359CrossRefMATHMathSciNet
16.
Tao F, Zhang L, Zhang ZH, Nee AYC (2010) A quantum multi-agent evolutionary algorithm for selection of partners in a virtual enterprise. CIRP Ann Manufact Technol 59(1):485–488
17.
Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 congress on evolutionary computation, vol 1, pp 101–106
18.
Horn J, Nafpliotis N, Goldberg DE (1994) A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the 1st IEEE congress on evolutionary computation, vol 1, pp 82–87
19.
Wang DW, Yung KL, Lp WH (2001) A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment. IEEE Trans Syst Man Cybern Part C 31(2):189–198CrossRef
20.
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef
21.
March JG (1991) Exploration and exploitation in organizational learning. Organ Sci v2(1):71–87
22.
23.
Zhang G, Gao L, Shi Y (2011) An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Syst Appl 38(4):3563–3573CrossRef
24.
Zhang G (2011) Quantum-inspired evolutionary algorithms: a survey and empirical study. J Heuristics 17(3):303–351CrossRefMATH
25.
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Kluwer Academic Publishers, BostonMATH
26.
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85CrossRef
27.
28.
Wang L, Pan J, Jiao LC (2000) The immune algorithm. ACTA Electronica Sinica 28(7):74–78
29.
Wang L, Pan J, Jiao LC (2000) The immune programming. Chin J Comput 23(8):806–812
30.
de Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251CrossRef
31.
Hofmeyr SA, Forrest S (2000) Architecture for an artificial immune system. Evol Comput 8(4):443–473CrossRef
32.
Noutani Y, Andresen B (1998) A comparison of simulated annealing cooling strategies. J Phys A: Math Gen 41(31):8373–8385
33.
Ali MM, Torn A, Viitanen S (2002) A direct search variant of the simulated annealing algorithm for optimization involving continuous variables. Comput Oper Res 29(1):87–102CrossRefMATHMathSciNet
34.
Varanelli JM (1996) On the acceleration of simulated annealing. University of Virginia, USA
35.
Lourenco HR, Martin O, Stutzle T (2003) Iterated local search. Int Ser Oper Res Manag Sci 57:321–353 (Handbook of Metaheuristics. Kluwer Academic Publishers)
36.
Lourenco HR, Martin O, Stutzle T (2010) Iterated local search: framework and applications. Int Ser Oper Res Manag Sci 146:363–397 (Handbook of Metaheuristics, 2nd edn. Kluwer Academic Publishers)
37.
Fanjul-Peyro L, Ruiz R (2010) Iterated greedy local search methods for unrelated parallel machine scheduling. Eur J Oper Res 207(1):55–69CrossRefMATHMathSciNet
38.
Derbel H, Jarboui B, Hanafi S, Chabchoub H (2012) Genetic algorithm with iterated local search for solving a location-routing problem. Expert Syst Appl 39(3):2865–2871CrossRef
39.
Dorigo M, Maniezzo V, Colorn A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26(1):29–42CrossRef
40.
Dorigo M, Gambardella M (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRef
41.
Stutzle T, Hoos HH (2000) MAX-MIN ant system. Future Gener Comput Syst 16(8):889–914CrossRef
42.
Birattari M, Pellegrini P, Dorigo M (2007) On the invariance of ant colony optimization. IEEE Trans Evol Comput 11(6):732–742CrossRef
43.
Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M, Baesens B (2007) Classification with ant colony optimization. IEEE Trans Evol Comput 11(5):651–665CrossRef
44.
Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33(3):859–871CrossRefMATH
45.
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(2):58–73CrossRef
46.
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: IEEE 2th proceedings of evolutionary computation, pp 1671–1676
47.
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation
48.
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471CrossRefMATHMathSciNet
49.
Platel MD, Schliebs S, Kasabov N (2009) Quantum-inspired evolutionary algorithm: a multimodel EDA. IEEE Trans Evol Comput 13(6):1218–1232CrossRef
50.
Lam AYS, Li VOK (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399CrossRef
51.
Wang C, Cheng HZ (2008) Optimization of network configuration in large distribution systems using plant growth simulation algorithm. IEEE Trans Power Syst 23(1):119–126CrossRef
52.
Daskin A, Kais S (2011) Group leaders optimization algorithm. Mol Pheys 109(5):761–772CrossRef
53.
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366CrossRef
54.
Yang XS(2008) Nature-inspired metaheuristic algorithms. Luniver Press
55.
Muhlenbein H, Schomisch M, Born J (1991) The parallel genetic algorithm as function optimizer[J]. Parallel Comput 17(6–7):619–632CrossRef
56.
Yang HT, Yang PC, Huang CL (1997) A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks. IEEE Trans Power Syst 12(2):661–668CrossRef
57.
Fukuyama Y, Chiang HD (1996) A parallel genetic algorithm for generation expansion planning. IEEE Trans Power Syst 11(2):955–961CrossRef
58.
Xu DJ, Daley ML (1995) Design of optimal digital-filter using a parallel genetic algorithm. IEEE Trans Circ Syst 42(10):673–675CrossRef
59.
Matsumura T, Nakamura M, Okech J, Onaga K (1998) A parallel and distributed genetic algorithm on loosely-coupled multiprocessor system. IEICE Trans Fundam Elect Commun Comput Sci 81(4):540–546
60.
Yeung SH, Chan WS, Ng KT, Man KF (2012) Computational optimization algorithms for antennas and RF/microwave circuit designs: an overview. IEEE Trans Industr Inf 8(2):216–227CrossRef
61.
Tao F, Zhao DM, Hu YF, Zhou ZD (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Industr Inf 4(4):315–327CrossRef
62.
Tang KS, Yin RJ, Kwong S, Ng KT, Man KF (2011) A theoretical development and analysis of jumping gene genetic algorithm. IEEE Trans Industr Inf 7(3):408–418CrossRef
63.
Lo CH, Fung EHK, Wong YK (2009) Intelligent automatic fault detection for actuator failures in aircraft. IEEE Trans Industr Inf 5(1):50–55CrossRef
64.
Hur SH, Katebi R, Taylor A (2011) Modeling and control of a plastic film manufacturing web process. IEEE Trans Industr Inf 7(2):171–178CrossRef
65.
Wolpert DH (1997) W G Macready (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefMathSciNet
66.
Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press
67.
68.
69.
Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica D 22(1–3):187–204CrossRefMathSciNet
70.
Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milanno
71.
Adleman LM (1994) Molecular computation of solutions to combinatorial problem. Science 266(5187):1021–1024CrossRef
72.
Reynolds RG (1994) An introduction to cultural algorithms. In: The 3rd annual conference on evolution programming
73.
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks
74.
Linhares A (1998) State-space search strategies gleaned from animal behavior: a traveling salesman experiment. Biol Cybern 87(3):167–173CrossRef
75.
Li XL (2003) A new intelligent optimization algorithm—artificial fish school algorithm. Ph.D. Thesis, Zhejiang University, China
76.
Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, Berlin, p 65–74
Metadaten
Titel
Brief History and Overview of Intelligent Optimization Algorithms
verfasst von
Fei Tao
Yuanjun Laili
Lin Zhang
Copyright-Jahr
2015
Buch
Configurable Intelligent Optimization Algorithm

Print ISBN: 978-3-319-08839-6

Electronic ISBN: 978-3-319-08840-2

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-08840-2_1