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
Engineering with Computers
Erschienen in: Engineering with Computers 3/2018

25.11.2017 | Original Article

Solving randomized time-varying knapsack problems by a novel global firefly algorithm

verfasst von: Yanhong Feng, Gai-Ge Wang, Ling Wang

Erschienen in: Engineering with Computers | Ausgabe 3/2018

Einloggen

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

search-config
loading …

Abstract

In this paper, a novel global firefly algorithm (GFA) is proposed for solving randomized time-varying knapsack problems (RTVKP). The RTVKP is an extension from the generalized time-varying knapsack problems (TVKP), by dynamically changing the profit and weight of items as well as the capacity of knapsack. In GFA, two-tuples which consists of real vector and binary vector is used to represent the individual in a population, and two principal search processes are developed: the current global best-based search process and the trust region-based search process. Moreover, a novel and effective two-stage repair operator is adopted to modify infeasible solutions and optimize feasible solutions as well. The performance of GFA is verified by comparison with five state-of-the-art classical algorithms over three RTVKP instances. The results indicate that the proposed GFA outperform the other five methods in most cases and that GFA is an efficient algorithm for solving randomized time-varying knapsack problems.
Vorheriger Artikel Capillary formation in tumor angiogenesis through meshless weak and strong local radial point interpolation
Nächster Artikel Enhanced optimization-based structural damage detection method using modal strain energy and modal frequencies

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

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+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
Literatur
1.
Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3):303–317CrossRef
2.
Weicker K (2003) Evolutionary algorithms and dynamic optimization problems. Verlag, Der Andere
3.
Morrison RW (2004) Designing evolutionary algorithms for dynamic environments. Springer, BerlinCrossRefMATH
4.
Krishnakumar K (1990) Micro-genetic algorithms for stationary and non-stationary function optimization//1989 Advances in Intelligent Robotics Systems Conference. In: International Society for Optics Photonics pp 289–296
5.
Dréo J, Siarry P (2006) An ant colony algorithm aimed at dynamic continuous optimization. Appl Math Comput 181(1):457–467MathSciNetMATH
6.
Lepagnot J, Nakib A, Oulhadj H et al (2012) A new multi-agent algorithm for dynamic continuous optimization. Modeling, analysis, and applications in Metaheuristic Computing: advancements and trends: advancements and trends, p 131
7.
Li C, Yang M, Kang L (2006) A new approach to solving dynamic traveling salesman problems. In: Simulated evolution and learning, lecture notes in computer science 4247: pp 236–243
8.
Uyar AS, Harmanci AE (2005) A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments. Soft Comput 9(11):803–814CrossRefMATH
9.
Goldberg DE, Smith RE (1987) Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: International conference on genetic algorithms. L. Erlbaum Associates Inc, Hillsdale, pp 59–68
10.
11.
He YC, Wang XZ, Li WB et al (2017) Exact algorithms and evolutionary algorithms for randomized time-varying knapsack problems. J Softw 28(2):185–202MathSciNetMATH
12.
Yang XS, Cui Z, Xiao R et al (2013) Swarm intelligence and bio-inspired computation: theory and applications. Elsevier insights
13.
Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154MathSciNetCrossRef
14.
Bhattacharjee KK, Sarmah SP (2014) Shuffled frog leaping algorithm and its application to 0/1 knapsack problem. ApplSoft Comput J 19:252–263
15.
Li X, Zhang J, Yin M (2013) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 1–11
16.
Gandomi AH, Yang XS, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255CrossRef
17.
Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483CrossRef
18.
Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceeding of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), IEEE Publications, pp 210–214
19.
Wang GG, Guo LH, Wang HQ et al (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871CrossRef
20.
21.
Wang GG, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370CrossRef
22.
Cui ZH, Fan S, Zeng J et al (2013) Artificial plant optimization algorithm with three-period photosynthesis. Int J Bio Inspired Comput 5(2):133–139CrossRef
23.
24.
Feng YH, Wang G-G, Deb S, Lu M, Zhao XJ (2017) Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization. Neural Comput Appl 28(7):1619–1634CrossRef
25.
26.
Gandomi AH, Yang XS, Alavi AH (2011) Mixed variable structural optimization using Firefly Algorithm. Comput Struct 89(23–24):2325–2336CrossRef
27.
28.
Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver
29.
Fister I, Fister I Jr, Yang XS et al (2013) A comprehensive review of firefly algorithms. Swarm Evolut Comput 13:34–46CrossRefMATH
30.
Baykasoğlu A, Ozsoydan FB (2014) An improved firefly algorithm for solving dynamic multidimensional knapsack problems. Expert Syst Appl 41(8):3712–3725CrossRef
31.
Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefMATH
32.
33.
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, BerlinCrossRefMATH
34.
35.
Du DZ, Ko KI (2011) Theory of computational complexity. Wiley, HobokenMATH
36.
Brassard G, Bratley P (1996) Fundamentals of algorithmics. Prentice Hall, Englewood CliffsMATH
37.
Pisinger D (1995) Algorithms for knapsack problems. N-Holl Math Stud 132:213–257MathSciNet
38.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE International Conference on Neural Networks, pp 1942–1948
39.
Zou D, Gao L, Li S et al (2011) Solving 0–1 knapsack problem by a novel global harmony search algorithm. Appl Soft Comput J 11(2):1556–1564CrossRef
40.
He YC, Wang XZ, Kou YZ (2007) A binary differential evolution algorithm with hybrid encoding. J Comput Res Dev 44(9):1476–1484CrossRef
41.
42.
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83CrossRef
43.
Wilcoxon F, Katti SK, Wilcox RA (1970) Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Sel Tables Math Stat 1:171–259MATH
44.
Bhattacharjee KK, Sarmah SP (2015) A binary firefly algorithm for knapsack problems// Industrial Engineering and Engineering Management (IEEM), 2015 IEEE International Conference on IEEE
45.
Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA’s//Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence. In: Proceedings of the First IEEE Conference on. IEEE, pp 579–584
46.
Olsen AL (1994) Penalty functions and the knapsack problem//Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence. Proceedings of the First IEEE Conference on IEEE, pp 554–558
47.
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, BostonMATH
48.
Simon D (2013) Evolutionary optimization algorithms. Wiley, New York
49.
He YC, Song JM, Zhang JM et al (2015) Research on genetic algorithms for solving static and dynamic knapsack problems. Appl Res Comput 32(4):1011–1015
50.
He YC, Wang XZ, He YL et al (2016) Exact and approximate algorithms for discounted {0–1 knapsack problem. Inf Sci 369:634–647MathSciNetCrossRef
51.
Metadaten
Titel
Solving randomized time-varying knapsack problems by a novel global firefly algorithm
verfasst von
Yanhong Feng
Gai-Ge Wang
Ling Wang
Publikationsdatum
25.11.2017
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 3/2018
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-017-0562-6

Weitere Artikel der Ausgabe 3/2018

Engineering with Computers 3/2018 Zur Ausgabe

Original Article

An intelligent method for generating artificial earthquake records based on hybrid PSO–parallel brain emotional learning inspired model

Original Article

Steady-state, nonlinear analysis of large arrays of electrically actuated micromembranes vibrating in a fluid

Original Article

Numerical techniques for solving system of nonlinear inverse problem

Original Article

Hierarchical template-based hexahedral mesh generation

Original Article

Enhanced optimization-based structural damage detection method using modal strain energy and modal frequencies

Original Article

An enhanced approach to automatic decomposition of thin-walled components for hexahedral-dominant meshing

Neuer Inhalt

    Bildnachweise