A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem

Rong Hao; Jia Zhang; Bin Xin; Chen Chen; Lihua Dou

doi:10.1145/3205651.3205732

A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem

Rong Hao, Jia Zhang, Bin Xin^*, Chen Chen, Lihua Dou

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively.

Original language	English
Title of host publication	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery, Inc
Pages	251-252
Number of pages	2
ISBN (Electronic)	9781450357647
DOIs	https://doi.org/10.1145/3205651.3205732
Publication status	Published - 6 Jul 2018
Event	2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018

Publication series

Name	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Conference

Conference	2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Country/Territory	Japan
City	Kyoto
Period	15/07/18 → 19/07/18

Keywords

Differential evolution
Estimation of distribution algorithm
Hybridization
Multi-point dynamic aggregation

Access to Document

10.1145/3205651.3205732

Cite this

Hao, R., Zhang, J., Xin, B., Chen, C., & Dou, L. (2018). A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 251-252). (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3205651.3205732

Hao, Rong ; Zhang, Jia ; Xin, Bin et al. / A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. pp. 251-252 (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion).

@inproceedings{1c69f242f1cb4b6eb3b5dfc3dd8401f8,

title = "A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem",

abstract = "The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively.",

keywords = "Differential evolution, Estimation of distribution algorithm, Hybridization, Multi-point dynamic aggregation",

author = "Rong Hao and Jia Zhang and Bin Xin and Chen Chen and Lihua Dou",

note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 ; Conference date: 15-07-2018 Through 19-07-2018",

year = "2018",

month = jul,

day = "6",

doi = "10.1145/3205651.3205732",

language = "English",

series = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",

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Hao, R, Zhang, J, Xin, B , Chen, C & Dou, L 2018, A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem. in GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc, pp. 251-252, 2018 Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, 15/07/18. https://doi.org/10.1145/3205651.3205732

A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem. / Hao, Rong; Zhang, Jia; Xin, Bin et al.
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2018. p. 251-252 (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem

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AU - Zhang, Jia

AU - Xin, Bin

AU - Chen, Chen

AU - Dou, Lihua

PY - 2018/7/6

Y1 - 2018/7/6

N2 - The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively.

AB - The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively.

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KW - Estimation of distribution algorithm

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Hao R, Zhang J, Xin B , Chen C, Dou L. A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc. 2018. p. 251-252. (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). doi: 10.1145/3205651.3205732

A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem

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Keywords

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