Hybrid estimation of distribution algorithm for multiobjective knapsack problem

Hui Li; Qingfu Zhang; Edward Tsang; John A. Ford

doi:10.1007/978-3-540-24652-7_15

Hybrid estimation of distribution algorithm for multiobjective knapsack problem

Hui Li
, Qingfu Zhang
, Edward Tsang
, John A. Ford

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

61 Citations (Scopus)

Abstract

We propose a hybrid estimation of distribution algorithm (MOHEDA) for solving the multiobjective 0/1 knapsack problem (MOKP). Local search based on weighted sum method is proposed, and random repair method (RRM) is used to handle the constraints. Moreover, for the purpose of diversity preservation, a new and fast clustering method, called stochastic clustering method (SCM), is also introduced for mixture-based modelling. The experimental results indicate that MOHEDA outperforms several other state-of-the-art algorithms. © Springer-Verlag Berlin Heidelberg 2004

Original language	English
Title of host publication	Evolutionary Computation in Combinatorial Optimization
Subtitle of host publication	4th European Conference, EvoCOP 2004, Coimbra, Portugal, April 5-7, 2004, Proceedings
Editors	Jens Gottlieb, Günther R. Raidl
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	145-154
ISBN (Electronic)	978-3-540-24652-7
ISBN (Print)	978-3-540-21367-3
DOIs	https://doi.org/10.1007/978-3-540-24652-7_15
Publication status	Published - 2004
Externally published	Yes
Event	4th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004) - Coimbra, Portugal Duration: 5 Apr 2004 → 7 Apr 2004

Publication series

Name	Lecture Notes in Computer Science
Volume	3004
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004)
Place	Portugal
City	Coimbra
Period	5/04/04 → 7/04/04

Access Output

10.1007/978-3-540-24652-7_15

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Li, H., Zhang, Q., Tsang, E., & Ford, J. A. (2004). Hybrid estimation of distribution algorithm for multiobjective knapsack problem. In J. Gottlieb, & G. R. Raidl (Eds.), Evolutionary Computation in Combinatorial Optimization: 4th European Conference, EvoCOP 2004, Coimbra, Portugal, April 5-7, 2004, Proceedings (pp. 145-154). (Lecture Notes in Computer Science; Vol. 3004). Springer . https://doi.org/10.1007/978-3-540-24652-7_15

Li, Hui ; Zhang, Qingfu ; Tsang, Edward et al. / Hybrid estimation of distribution algorithm for multiobjective knapsack problem. Evolutionary Computation in Combinatorial Optimization: 4th European Conference, EvoCOP 2004, Coimbra, Portugal, April 5-7, 2004, Proceedings. editor / Jens Gottlieb ; Günther R. Raidl. Berlin, Heidelberg : Springer , 2004. pp. 145-154 (Lecture Notes in Computer Science).

@inproceedings{955b9e27c39346ff9df4302eb6df83d3,

title = "Hybrid estimation of distribution algorithm for multiobjective knapsack problem",

abstract = "We propose a hybrid estimation of distribution algorithm (MOHEDA) for solving the multiobjective 0/1 knapsack problem (MOKP). Local search based on weighted sum method is proposed, and random repair method (RRM) is used to handle the constraints. Moreover, for the purpose of diversity preservation, a new and fast clustering method, called stochastic clustering method (SCM), is also introduced for mixture-based modelling. The experimental results indicate that MOHEDA outperforms several other state-of-the-art algorithms. {\textcopyright} Springer-Verlag Berlin Heidelberg 2004",

author = "Hui Li and Qingfu Zhang and Edward Tsang and Ford, \{John A.\}",

year = "2004",

doi = "10.1007/978-3-540-24652-7\_15",

language = "English",

isbn = "978-3-540-21367-3",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "145--154",

editor = "Gottlieb, \{Jens \} and Raidl, \{G{\"u}nther R. \}",

booktitle = "Evolutionary Computation in Combinatorial Optimization",

note = "4th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004) ; Conference date: 05-04-2004 Through 07-04-2004",

}

Li, H, Zhang, Q, Tsang, E & Ford, JA 2004, Hybrid estimation of distribution algorithm for multiobjective knapsack problem. in J Gottlieb & GR Raidl (eds), Evolutionary Computation in Combinatorial Optimization: 4th European Conference, EvoCOP 2004, Coimbra, Portugal, April 5-7, 2004, Proceedings. Lecture Notes in Computer Science, vol. 3004, Springer , Berlin, Heidelberg, pp. 145-154, 4th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004), Coimbra, Portugal, 5/04/04. https://doi.org/10.1007/978-3-540-24652-7_15

Hybrid estimation of distribution algorithm for multiobjective knapsack problem. / Li, Hui; Zhang, Qingfu; Tsang, Edward et al.
Evolutionary Computation in Combinatorial Optimization: 4th European Conference, EvoCOP 2004, Coimbra, Portugal, April 5-7, 2004, Proceedings. ed. / Jens Gottlieb; Günther R. Raidl. Berlin, Heidelberg: Springer , 2004. p. 145-154 (Lecture Notes in Computer Science; Vol. 3004).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Hybrid estimation of distribution algorithm for multiobjective knapsack problem

AU - Li, Hui

AU - Zhang, Qingfu

AU - Tsang, Edward

AU - Ford, John A.

PY - 2004

Y1 - 2004

N2 - We propose a hybrid estimation of distribution algorithm (MOHEDA) for solving the multiobjective 0/1 knapsack problem (MOKP). Local search based on weighted sum method is proposed, and random repair method (RRM) is used to handle the constraints. Moreover, for the purpose of diversity preservation, a new and fast clustering method, called stochastic clustering method (SCM), is also introduced for mixture-based modelling. The experimental results indicate that MOHEDA outperforms several other state-of-the-art algorithms. © Springer-Verlag Berlin Heidelberg 2004

AB - We propose a hybrid estimation of distribution algorithm (MOHEDA) for solving the multiobjective 0/1 knapsack problem (MOKP). Local search based on weighted sum method is proposed, and random repair method (RRM) is used to handle the constraints. Moreover, for the purpose of diversity preservation, a new and fast clustering method, called stochastic clustering method (SCM), is also introduced for mixture-based modelling. The experimental results indicate that MOHEDA outperforms several other state-of-the-art algorithms. © Springer-Verlag Berlin Heidelberg 2004

UR - https://www.scopus.com/pages/publications/35048822051

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-35048822051&origin=recordpage

U2 - 10.1007/978-3-540-24652-7_15

DO - 10.1007/978-3-540-24652-7_15

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-540-21367-3

T3 - Lecture Notes in Computer Science

SP - 145

EP - 154

BT - Evolutionary Computation in Combinatorial Optimization

A2 - Gottlieb, Jens

A2 - Raidl, Günther R.

PB - Springer

CY - Berlin, Heidelberg

T2 - 4th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004)

Y2 - 5 April 2004 through 7 April 2004

ER -

Li H, Zhang Q, Tsang E, Ford JA. Hybrid estimation of distribution algorithm for multiobjective knapsack problem. In Gottlieb J, Raidl GR, editors, Evolutionary Computation in Combinatorial Optimization: 4th European Conference, EvoCOP 2004, Coimbra, Portugal, April 5-7, 2004, Proceedings. Berlin, Heidelberg: Springer . 2004. p. 145-154. (Lecture Notes in Computer Science). Epub 2004 Mar 9. doi: 10.1007/978-3-540-24652-7_15

Hybrid estimation of distribution algorithm for multiobjective knapsack problem

Abstract

Publication series

Conference

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this