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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Automatic Generation of Cyber Architectures Optimized for Security, Cost, and Mission Performance: A Nature-Inspired Approach Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Modified Krill Herd Algorithm for Global Numerical Optimization Problems

verfasst von : Laith Mohammad Abualigah, Ahamad Tajudin Khader, Essam Said Hanandeh

Erschienen in: Advances in Nature-Inspired Computing and Applications

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

For the purpose of improving the search strategy of the krill herd algorithm (KHA), an improved robust approach is proposed to address the function optimization problems, namely, modified krill herd algorithm (MKHA). In MKHA method, the modification of krill herd algorithm focuses on genetic operators (GOs) and it occurs in the ordering of procedures of the basic krill herd algorithm, where the crossover and mutation operators are employed after the updating process of the krill individuals position, the krill herd (KH) motion calculations, is finished. This modification is conducted because the genetic operators insignificantly exploit to enhance the global exploration search in the basic krill herd algorithm so as to speed up convergence. Several versions of benchmark functions are applied to verify the proposed method (MKHA) and it is showed that, in most cases, the proposed algorithm (MKHA) obtained better results in comparison with the basic KHA and other comparative methods.

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
Vorheriges Kapitel An Energy-Efficient Cluster Head Selection Using Artificial Bees Colony Optimization for Wireless Sensor Networks
Nächstes Kapitel Application of Nature-Inspired Optimization Techniques in Vessel Traffic Control
Literatur
1.
Ghanem WAHM, Jantan A (2017) An enhanced Bat algorithm with mutation operator for numerical optimization problems. Neural Comput Appl 1–35
2.
Alomari OA, Khader AT, Mohammed A, Abualigah LM, Nugroho H, Chandra GR et al (2017) Mrmr Ba: a hybrid gene selection algorithm for cancer classification. J Theor Appl Inf Technol 95(12):1
3.
Shehab M, Khader AT, Al-Betar MA, Abualigah LM (2017) Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In: 2017 8th international conference on information technology (ICIT). IEEE, pp 36–43
4.
Wang GG, Guo L, Duan H, Wang H (2014) A new improved firefly algorithm for global numerical optimization. J Comput Theor Nanosci 11(2):477–485CrossRef
5.
Trivedi IN, Gandomi AH, Jangir P, Kumar A, Jangir N, Totlani R (2017) Adaptive krill herd algorithm for global numerical optimization. In: Advances in computer and computational sciences. Springer, pp 517–525
6.
Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math 2013:21MathSciNetMATH
7.
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435CrossRef
8.
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845MathSciNetCrossRef
9.
Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125CrossRef
10.
Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48:1–25CrossRef
11.
Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci, Eng Appl 5(1):19
12.
Abualigah LM, Khader AT, AlBetar MA, Hanandeh ES (2017) A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering
13.
Abualigah LM, Khader AT, Al-Betar MA, Alomari OA (2017) Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst Appl 84:24–36CrossRef
14.
Abualigah LM, Khader AT, Al-Betar MA. Unsupervised feature selection technique based on genetic algorithm for improving the text clustering. In: 2016 7th international conference on computer science and information technology (CSIT), pp. 1–6. IEEE
15.
Abualigah LM, Khader AT, AlBetar MA, Hanandeh ES (2017) Unsupervised text feature selection technique based on particle swarm optimization algorithm for improving the text clustering. EAI
16.
Xinchao Z (2010) A perturbed particle swarm algorithm for numerical optimization. Appl Soft Comput 10(1):119–124CrossRef
17.
Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37(8):5682–5687CrossRef
18.
Chuang LY, Tsai SW, Yang CH (2011) Chaotic catfish particle swarm optimization for solving global numerical optimization problems. Appl Math Comput 217(16):6900–6916MathSciNetMATH
19.
Ghanem WA, Jantan A (2016) Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems. Neural Comput Appl 1–19
20.
Sakib N, Kabir MWU, Subbir M, Alam S (2014) A comparative study of flower pollination algorithm and bat algorithm on continuous optimization problems. Int J Soft Comput Eng 4(3):13–19
21.
Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871CrossRef
22.
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 1–23
23.
Shehab M, Khader AT, Laouchedi M (2017) Modified cuckoo search algorithm for solving global optimization problems. In: International conference of reliable information and communication technology, pp. 561–570. Springer
24.
Wang GG, Hossein Gandomi A, Hossein Alavi A (2013) A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes 42(6):962–978MathSciNetCrossRef
25.
Guo L, Wang GG, Gandomi AH, Alavi AH, Duan H (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138:392–402CrossRef
26.
Wang GG, Gandomi AH, Alavi AH, Hao GS (2014) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25(2):297–308CrossRef
27.
Abualigah LM, Khader AT, Al-Betar MA, Awadallah MA (2016) A krill herd algorithm for efficient text documents clustering. In: 2016 IEEE symposium on computer applications & industrial electronics (ISCAIE), pp. 67–72. IEEE
28.
Wang GG, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Modell 38(9):2454–2462MathSciNetCrossRef
29.
30.
Abualigah LM, Khader AT, Hanandeh ES (2018) A hybrid strategy for krill herd algorithm with harmony search algorithm to improve the data clustering. Intell Decis Technol 12:1–12CrossRef
31.
Bolaji AL, Al-Betar MA, Awadallah MA, Khader AT, Abualigah LM (2016) A comprehensive review: Krill Herd algorithm (KH) and its applications. Applied Soft Computing. 49:437–446CrossRef
32.
Jensi R, Jiji GW (2016) An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Applied Soft Computing. 46:230–245CrossRef
33.
Wang GG, Gandomi AH, Zhao X, Chu HCE (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Computing. 20(1):273–285CrossRef
Metadaten
Titel
Modified Krill Herd Algorithm for Global Numerical Optimization Problems
verfasst von
Laith Mohammad Abualigah
Ahamad Tajudin Khader
Essam Said Hanandeh
Copyright-Jahr
2019
Buch
Advances in Nature-Inspired Computing and Applications

Print ISBN: 978-3-319-96450-8

Electronic ISBN: 978-3-319-96451-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-96451-5_9

Neuer Inhalt

    Bildnachweise