MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems

Ahmad Alhindi; Qingfu Zhang

doi:10.1109/CEC.2014.6900413

MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems

Department of Computer Science

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

29 Citations (Scopus)

Abstract

Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation problem into a number of single-objective problems and optimises them in a collaborative manner. This paper investigates how to use Tabu Search (TS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the TS is applied to these subproblems with the aim to escape from local optimal solutions. The experimental studies have shown that MOEA/D with TS outperforms the classical MOEA/D on multiobjective permutation flow shop scheduling problems. It also have demonstrated that use of problem specific knowledge can significantly improve the algorithm performance.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	IEEE
Pages	1155-1164
ISBN (Print)	9781479914883
DOIs	https://doi.org/10.1109/CEC.2014.6900413
Publication status	Published - Jul 2014
Event	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Conference

Conference	2014 IEEE Congress on Evolutionary Computation, CEC 2014
Place	China
City	Beijing
Period	6/07/14 → 11/07/14

Research Keywords

Decomposition
multiobjective optimisation
Tabu search

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10.1109/CEC.2014.6900413

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Cite this

@inproceedings{63ec178edb094c789ddcd3594fa59f85,

title = "MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems",

abstract = "Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation problem into a number of single-objective problems and optimises them in a collaborative manner. This paper investigates how to use Tabu Search (TS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the TS is applied to these subproblems with the aim to escape from local optimal solutions. The experimental studies have shown that MOEA/D with TS outperforms the classical MOEA/D on multiobjective permutation flow shop scheduling problems. It also have demonstrated that use of problem specific knowledge can significantly improve the algorithm performance.",

keywords = "Decomposition, multiobjective optimisation, Tabu search",

author = "Ahmad Alhindi and Qingfu Zhang",

year = "2014",

month = jul,

doi = "10.1109/CEC.2014.6900413",

language = "English",

isbn = "9781479914883",

pages = "1155--1164",

booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

publisher = "IEEE",

address = "United States",

note = "2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

}

MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems. / Alhindi, Ahmad; Zhang, Qingfu.
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. IEEE, 2014. p. 1155-1164 6900413.

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

TY - GEN

T1 - MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems

AU - Alhindi, Ahmad

AU - Zhang, Qingfu

PY - 2014/7

Y1 - 2014/7

N2 - Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation problem into a number of single-objective problems and optimises them in a collaborative manner. This paper investigates how to use Tabu Search (TS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the TS is applied to these subproblems with the aim to escape from local optimal solutions. The experimental studies have shown that MOEA/D with TS outperforms the classical MOEA/D on multiobjective permutation flow shop scheduling problems. It also have demonstrated that use of problem specific knowledge can significantly improve the algorithm performance.

AB - Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation problem into a number of single-objective problems and optimises them in a collaborative manner. This paper investigates how to use Tabu Search (TS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the TS is applied to these subproblems with the aim to escape from local optimal solutions. The experimental studies have shown that MOEA/D with TS outperforms the classical MOEA/D on multiobjective permutation flow shop scheduling problems. It also have demonstrated that use of problem specific knowledge can significantly improve the algorithm performance.

KW - Decomposition

KW - multiobjective optimisation

KW - Tabu search

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U2 - 10.1109/CEC.2014.6900413

DO - 10.1109/CEC.2014.6900413

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

SN - 9781479914883

SP - 1155

EP - 1164

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - IEEE

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems

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