Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem

Irina Petrova; Arina Buzdalova; Maxim Buzdalov

doi:10.1109/ICMLA.2013.151

Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem

Irina Petrova, Arina Buzdalova, Maxim Buzdalov

ITMO University

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

6 Citations (Scopus)

Abstract

A single-objective optimization problem can be solved more efficiently by introducing some helper-objectives and running a multi-objective evolutionary algorithm. But what objectives should be used at each optimization stage? This paper describes a new method of adaptive helper-objectives selection in multi-objective evolutionary algorithms. The proposed method is applied to the Job-Shop scheduling problem and compared with the previously known approach, which was specially developed for the Job-Shop problem. A comparison with the previously proposed method of adaptive helper-objective selection based on reinforcement learning is performed as well.

Original language	English
Title of host publication	ICMLA '13
Subtitle of host publication	Proceedings of the 2013 12th International Conference on Machine Learning and Applications
Pages	374-377
Number of pages	4
Volume	2
ISBN (Electronic)	978-0-7695-5144-9
DOIs	https://doi.org/10.1109/ICMLA.2013.151
Publication status	Published - 04 Dec 2013
Externally published	Yes
Event	2013 12th International Conference on Machine Learning and Applications - Miami, United States of America Duration: 04 Dec 2013 → 07 Dec 2013

Conference

Conference	2013 12th International Conference on Machine Learning and Applications
Country/Territory	United States of America
City	Miami
Period	04 Dec 2013 → 07 Dec 2013

Keywords

job-shop problem
helper-objectives
multi-objective optimization
adaptive selection

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10.1109/ICMLA.2013.151

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@inproceedings{a756592c4dae49acb95ba0d74e865a5a,

title = "Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem",

abstract = "A single-objective optimization problem can be solved more efficiently by introducing some helper-objectives and running a multi-objective evolutionary algorithm. But what objectives should be used at each optimization stage? This paper describes a new method of adaptive helper-objectives selection in multi-objective evolutionary algorithms. The proposed method is applied to the Job-Shop scheduling problem and compared with the previously known approach, which was specially developed for the Job-Shop problem. A comparison with the previously proposed method of adaptive helper-objective selection based on reinforcement learning is performed as well.",

keywords = "job-shop problem, helper-objectives, multi-objective optimization, adaptive selection",

author = "Irina Petrova and Arina Buzdalova and Maxim Buzdalov",

year = "2013",

month = dec,

day = "4",

doi = "10.1109/ICMLA.2013.151",

language = "English",

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booktitle = "ICMLA '13",

note = "2013 12th International Conference on Machine Learning and Applications ; Conference date: 04-12-2013 Through 07-12-2013",

}

Petrova, I, Buzdalova, A & Buzdalov, M 2013, Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem. in ICMLA '13: Proceedings of the 2013 12th International Conference on Machine Learning and Applications. vol. 2, pp. 374-377, 2013 12th International Conference on Machine Learning and Applications, Miami, Florida, United States of America, 04 Dec 2013. https://doi.org/10.1109/ICMLA.2013.151

TY - GEN

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AU - Petrova, Irina

AU - Buzdalova, Arina

AU - Buzdalov, Maxim

PY - 2013/12/4

Y1 - 2013/12/4

N2 - A single-objective optimization problem can be solved more efficiently by introducing some helper-objectives and running a multi-objective evolutionary algorithm. But what objectives should be used at each optimization stage? This paper describes a new method of adaptive helper-objectives selection in multi-objective evolutionary algorithms. The proposed method is applied to the Job-Shop scheduling problem and compared with the previously known approach, which was specially developed for the Job-Shop problem. A comparison with the previously proposed method of adaptive helper-objective selection based on reinforcement learning is performed as well.

AB - A single-objective optimization problem can be solved more efficiently by introducing some helper-objectives and running a multi-objective evolutionary algorithm. But what objectives should be used at each optimization stage? This paper describes a new method of adaptive helper-objectives selection in multi-objective evolutionary algorithms. The proposed method is applied to the Job-Shop scheduling problem and compared with the previously known approach, which was specially developed for the Job-Shop problem. A comparison with the previously proposed method of adaptive helper-objective selection based on reinforcement learning is performed as well.

KW - job-shop problem

KW - helper-objectives

KW - multi-objective optimization

KW - adaptive selection

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84899429515&origin=resultslist

U2 - 10.1109/ICMLA.2013.151

DO - 10.1109/ICMLA.2013.151

M3 - Conference Proceeding (Non-Journal item)

VL - 2

SP - 374

EP - 377

BT - ICMLA '13

T2 - 2013 12th International Conference on Machine Learning and Applications

Y2 - 4 December 2013 through 7 December 2013

ER -

Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem

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