nach oben

Soft Computing

Erschienen in:

01.09.2015 | Methodologies and Application

A two-stage memory powered Great Deluge algorithm for global optimization

verfasst von: Adnan Acan, Ahmet Ünveren

Erschienen in: Soft Computing | Ausgabe 9/2015

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

A two-stage memory architecture and search operators exploiting the accumulated experience in memory are maintained within the framework of a Great DeLuge algorithm for real-valued global optimization. The level-based acceptance criterion of the Great DeLuge algorithm is applied for each best solution extracted in a particular iteration. The use of memory-based search supported by effective move operators results in a powerful optimization algorithm. The success of the presented approach is illustrated using three sets of well-known benchmark functions including problems of varying sizes and difficulties. Performance of the presented approach is evaluated and in comparison to well-known algorithms and their published results. Except for a few large-scale optimization problems, experimental evaluations demonstrated that the presented approach performs at least as good as its competitors.

Vorheriger Artikel Extended hesitant fuzzy hybrid weighted aggregation operators and their application in decision making

Nächster Artikel Water cycle algorithm for solving multi-objective optimization problems

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 "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

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

Abdullah S, Shaker K, McCollum B, McMullan P (2009) Construction of course timetables based on great deluge and tabu search. In: VIII Metaheuristic International Conference, pp x1–x12

Abdullah S, Turabieh H, McCollum B (2009) A hybridization of electromagnetic-like mechanism and great deluge for examination timetabling problems. In: Sixth international workshop on hybrid metaheuristics, pp 60–72

Abuhamdah A, Ayob M (2009) Hybridization multi-neighbourhood particle collision algorithm and great deluge for solving course timetabling problems. In: Conference on data mining and optimization, pp 108–114

Acan A (2005) An external partial permutations memory for ant colony optimization. In: Proceedings of evolutionary computation in combinatorial optimization-EvoCOP 2005, LNCS 3448, Springer, New york, pp 1–11

Acan A (2004) An external memory implementation in ant colony optimization. LNCS 3172:73–82

Al-Milli NR (2010) Hybrid genetic algorithms with great deluge for course timetabling. Int J Comput Sci Netw Secur 10(4):283–288

Burke E, Bykov Y, Newall J, Petrovic S (2003) A time-predefined local search approach to exam timetabling problems. CRC Press, Boca Raton, pp 76–90

Burke E, Bykov Y (2006) Solving exam timetabling problems with the flex-deluge algorithm. In: Proceedings of the practice and theory of automated timetabling conference (PATAT2006), pp 370–372

Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18CrossRef

Dueck G (1993) New optimization heuristics: the great deluge algorithm and the record-to-record travel. J Comput Phys 104:86–92CrossRef

Eshelman LJ (1991) The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination, foundations of genetic algorithms, pp 265–283

Ghatei S, Panahi FT, Hosseinzadeh M, Rouhi M, Rezazadeh I, Naebi A, Ghatei Z, Khajei RP (2012) A new hybrid algorithm for optimization using PSO and GDA. J Basic Appl Sci Res 2(3):2336–2341

Karaboga D, Bark B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471CrossRef

Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132MathSciNetCrossRef

Landa-Silva D, Obit JH (2009) Evolutionary non-linear great deluge for university course timetabling. In: International conference on hybrid intelligent systems, pp 269–276

Li JP, Balasz ME, Parks GT, Glarkson PJ (2002) A species conserving genetic algorithm for multimodal function optimization. Evol Comput 10(3):207–234CrossRef

Liang Y, Leung KS (2011) Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl Soft Comput 11:2017–2034

Mahfoud SW (1992) Crowding and preselection revisited, IlliGAL Report, No: 92004

McMullan P (2007) An extended implementation of the great deluge algorithm for course timetabling. In: International conference on computational science-ICCS2007, pp 538–545

Montgomery J, Randall M (2003) The accumulated experience ant colony for the traveling salesman problem. Int J Comput Intell Appl 3(2):189–198CrossRef

Nahas N, Nourelfath M, Kadi DA (2010) Iterated great deluge for the dynamic facility layout problem, technical report CIRRELT-2010-20. University of Montreal

Ozcan E, Misir M, Ochoa G, Burke EK (2010) A reinforcement learning—great deluge algorithm hyper-heuristic for examination timetabling. J Metaheuristic Comput 1(1):39–59CrossRef

Pavone M, Narzisi G, Nicosia G (2012) Clonal selection: an immunological algorithm for global optimization over continuous spaces. J Glob Optim 53(4):769–808

Picek S, Jakobovic D, Golub M (2013) On the recombination operator in the real-coded genetic algorithms. In: Proceedings of congress on evolutinary computation-CEC2013, pp 3103–3110

Ravi V (2004) Modified great deluge algorithm versus other metaheuristics in reliability optimization. Asia Pac J Oper Res 21(4):487–497

Shi Y, Eberhart RC, Empirical study of particle swarm optimization. In: Proceedings of the congress on evolutionary computation-CEC 1999, vol 3, pp 1945–1950

Silva D, Orbit JH (2008) Great deluge with non-linear decay rate for solving course timetabling problems. In: International IEEE conference on intelligent systems, pp 11–18

Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRef

Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization, technical report, KanGAL Report Number 2005005

Tang K, Li X, Suganthan PN, Yang Z, Weise T (2009) Benchmark functions for the CEC2010 special session and competition on large scale global optimization, technical report, nature inspired computation and applications laboratory, USTC, China. http://nical.ustc.edu.cn/cec10ss.php

Thierens D, Goldberg D (1994) Elitist recombination: an integrated selection and recombination GA. IEEE World Congr Comput Intell 1:508–512CrossRef

Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: IEEE conference on evolutionary computation-CEC2004, vol 2, pp 1980–1987

Whitley D, Kauth J (1988) GENITOR: a different genetic algorithm. In: Proceedings of the rocky mountain conference on artificial intelligence, pp 118–130

Yao X, Liu Y, Lin Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans EvolComput 3(2):82–102

Titel: A two-stage memory powered Great Deluge algorithm for global optimization
verfasst von: Adnan Acan
Ahmet Ünveren
Publikationsdatum: 01.09.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 9/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1423-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 9/2015

Image quantization using improved artificial fish swarm algorithm

Algebraic study to generalized Bosbach states on residuated lattices

Texture classification using feature selection and kernel-based techniques

Population statistics for particle swarm optimization: Single-evaluation methods in noisy optimization problems

Heuristic and genetic algorithms for solving survivability problem in the design of last mile communication networks

Constrained dynamic vehicle routing problems with time windows