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
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Research on Price Forecasting Method of China’s Carbon Trading Market Based on PSO-RBF Algorithm Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Hybrid Replacement Strategy for MOEA/D

verfasst von : Xiaoji Chen, Chuan Shi, Aimin Zhou, Siyong Xu, Bin Wu

Erschienen in: Bio-inspired Computing: Theories and Applications

Verlag: Springer Singapore

Einloggen

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

search-config
loading …

Abstract

In MOEA/D, the replacement strategy plays a key role in balancing diversity and convergence. However, existing adaptive replacement strategies either focus on neighborhood or global replacement strategy, which may have no obvious effects on balance of diversity and convergence in tackling complicated MOPs. In order to overcome this shortcoming, we propose a hybrid mechanism balancing neighborhood and global replacement strategy. In this mechanism, a probability threshold \( p_{t} \) is applied to determine whether to execute a neighborhood or global replacement strategy, which could balance diversity and convergence. Furthermore, we design an offspring generation method to generate the high-quality solution for each subproblem, which can ease mismatch between subproblems and solutions. Based on the classic MOEA/D, we design a new algorithm framework, called MOEA/D-HRS. Compared with other state-of-the-art MOEAs, experimental results show that the proposed algorithm obtains the best performance.

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 Dynamic Multimodal Optimization Using Brain Storm Optimization Algorithms
Nächstes Kapitel A Flexible Memristor-Based Neural Network
Literatur
1.
Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol. Comput. 1(1), 32–49 (2011)CrossRef
2.
Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)CrossRef
3.
4.
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). https://​doi.​org/​10.​1007/​BFb0056872CrossRef
5.
Laumanns, M.: SPEA2: improving the strength Pareto evolutionary algorithm. Eidgenössische Technische Hochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze (TIK) (2001)
6.
7.
Basseur, M., Zitzler, E.: A preliminary study on handling uncertainty in indicator-based multiobjective optimization. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 727–739. Springer, Heidelberg (2006). https://​doi.​org/​10.​1007/​11732242_​71CrossRef
8.
Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)CrossRef
9.
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRef
10.
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)CrossRef
11.
Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained mop test instances. In: 2009 IEEE Congress on Evolutionary Computation, pp. 203–208. IEEE (2009)
12.
Mashwani, W.K., Salhi, A.: A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation. Appl. Soft Comput. 12(9), 2765–2780 (2012)CrossRef
13.
Ma, X., et al.: MOEA/D with opposition-based learning for multiobjective optimization problem. Neurocomputing 146, 48–64 (2014)CrossRef
14.
Zhou, A., Zhang, Y., Zhang, G., Gong, W.: On neighborhood exploration and subproblem exploitation in decomposition based multiobjective evolutionary algorithms. In: 2017 IEEE Congress on Evolutionary Computation, pp. 1704–1711. IEEE (2015)
15.
Zhang, H., Zhou, A., Zhang, G., Singh, H.K.: Accelerating MOEA/D by Nelder-Mead method. In: 2017 IEEE Congress on Evolutionary Computation, pp. 976–983. IEEE (2017)
16.
17.
Lin, X., Zhang, Q., Kwong, S.: A decomposition based multiobjective evolutionary algorithm with classification. In: 2016 IEEE Congress on Evolutionary Computation, pp. 3292–3299. IEEE (2016)
18.
Zhang, J., Zhou, A., Tang, K., and Zhang, G.: Preselection via classification: a case study on evolutionary multiobjective optimization. arXiv:​1708.​01146 (2017)
19.
Chen, X., Shi, C., Zhou, A., Wu, B ., Cai, Z.: A decomposition based multi objective evolutionary algorithm with semi-supervised classification. In: 2017 IEEE Congress on Evolutionary Computation, pp. 797-804. IEEE (2017)
20.
Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Trans. Evol. Comput. 16(3), 442–446 (2012)CrossRef
21.
Li, K., Fialho, A., Kwong, S., Zhang, Q.: Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 18(1), 114–130 (2014)CrossRef
22.
Venske, S.M., GonçAlves, R.A., Delgado, M.R.: ADEMO/D: multiobjective optimization by an adaptive differential evolution algorithm. Neurocomputing 127(127), 65–77 (2014)CrossRef
23.
Lin, Q., et al.: A novel adaptive control strategy for decomposition-based multiobjective algorithm. Comput. Oper. Res. 78, 94–107 (2016)MathSciNetCrossRef
24.
Lin, Q., et al.: Adaptive composite operator selection and parameter control for multiobjective evolutionary algorithm. Inf. Sci. 339, 332–352 (2016)CrossRef
25.
Li, K., Zhang, Q., Kwong, S., Li, M., Wang, R.: Stable matching-based selection in evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 18(6), 909–923 (2014)CrossRef
26.
Li, K., Kwong, S., Zhang, Q., Deb, K.: Interrelationship-based selection for decomposition multiobjective optimization. IEEE Trans. Cybern. 45(10), 2076–2088 (2015)CrossRef
27.
Wang, Z., Zhang, Q., Zhou, A., Gong, M., Jiao, L.: Adaptive replacement strategies for MOEA/D. IEEE Trans. Cybern. 46(2), 474–486 (2017)CrossRef
28.
Tam, H.H., Leung, M.F., Wang, Z., Ng, S.C., Cheung, C.C., Lui, A.K.: Improved adaptive global replacement scheme for MOEA/D-AGR. In: 2016 IEEE congress on Evolutionary Computation, pp. 2153–2160. IEEE (2016)
29.
30.
Yu, C., Kelley L., Zheng, S., Tan Y.: Fireworks algorithm with differential mutation for solving the CEC 2014 competition problems. In: 2014 IEEE congress on Evolutionary Computation, pp. 3238–3245. IEEE (2014)
31.
Liu, L., Zheng, S., Tan, Y.: S-metric based multi-objective fireworks algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1257–1264 (2015)
32.
Cai, Z., Wang, Y.: A multiobjective optimization-based evolutionary algorithm for constrained optimization. IEEE Trans. Evol. Comput. 10(6), 658–675 (2006)CrossRef
33.
Tsutsui, S., Ghosh, A.: A study on the effect of multi-parent recombination in real coded genetic algorithms. In: IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp. 828–833 (1998)
34.
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRef
35.
Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Comput. Sci. Inform. 26, 30–45 (1996)
36.
Tizhoosh, H.R.: Opposition-based reinforcement learning. J. Adv. Comput. Intell. Intell. Inform. 10(4), 578–585 (2006)MathSciNetCrossRef
37.
Vapnik, V.N.: Statistical learning theory. Encycl. Sci. Learn. 41(4), 3185–3185 (1998)
38.
Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation, pp. 71–78. IEEE (2013)
39.
Mallipeddi, R., Wu, G., Lee, M., Suganthan, P.N.: Gaussian adaptation based parameter adaptation for differential evolution. In: 2014 IEEE Congress on Evolutionary Computation, pp. 1760–1767. IEEE (2014)
40.
Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput. 9(1), 3–12 (2005)MathSciNetCrossRef
41.
You, H., Yang, M., Wang, D., Jia, X.: Kriging model combined with Latin hypercube sampling for surrogate modeling of analog integrated circuit performance. In: International Symposium on Quality of Electronic Design, pp. 554–558. IEEE (2009)
42.
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999)MATH
43.
Li, Y., Zhou, A., Zhang, G.: An MOEA/D with multiple differential evolution mutation operators. In: 2014 IEEE Congress on Evolutionary Computation, pp. 397–404. IEEE (2014)
44.
Naujoks, B., Beume, N., Emmerich, M.: Multi-objective optimisation using S-metric selection: application to three-dimensional solution spaces. In: 2015 IEEE Congress on Evolutionary Computation, pp. 1282–1289. IEEE (2005)
Metadaten
Titel
A Hybrid Replacement Strategy for MOEA/D
verfasst von
Xiaoji Chen
Chuan Shi
Aimin Zhou
Siyong Xu
Bin Wu
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Bio-inspired Computing: Theories and Applications

Print ISBN: 978-981-13-2825-1

Electronic ISBN: 978-981-13-2826-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-13-2826-8_22

Premium Partner

    Bildnachweise