A Communication Strategy for Paralleling Grey Wolf Optimizer

Tien-Szu Pan; Thi-Kien Dao; Trong-The Nguyen; Shu-Chuan Chu

doi:10.1007/978-3-319-23207-2_25

A Communication Strategy for Paralleling Grey Wolf Optimizer

Tien-Szu Pan, Thi-Kien Dao, Trong-The Nguyen, Shu-Chuan Chu

Research output: Contribution to journal › Article › peer-review

32 Citations (Scopus)

Abstract

In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.

Original language	English
Pages (from-to)	253-262
Number of pages	10
Journal	Advances in Intelligent Systems and Computing
Volume	388
DOIs	https://doi.org/10.1007/978-3-319-23207-2_25
Publication status	Published - 2015
Event	The Ninth International Conference on Genetic and Evolutionary Computing (ICGEC 2015) - Duration: 27 Aug 2015 → …

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10.1007/978-3-319-23207-2_25

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title = "A Communication Strategy for Paralleling Grey Wolf Optimizer",

abstract = "In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.",

author = "Tien-Szu Pan and Thi-Kien Dao and Trong-The Nguyen and Shu-Chuan Chu",

year = "2015",

doi = "10.1007/978-3-319-23207-2_25",

language = "English",

volume = "388",

pages = "253--262",

journal = "Advances in Intelligent Systems and Computing",

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T1 - A Communication Strategy for Paralleling Grey Wolf Optimizer

AU - Pan, Tien-Szu

AU - Dao, Thi-Kien

AU - Nguyen, Trong-The

AU - Chu, Shu-Chuan

PY - 2015

Y1 - 2015

N2 - In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.

AB - In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.

UR - https://doi.org/10.1007/978-3-319-23207-2_25

U2 - 10.1007/978-3-319-23207-2_25

DO - 10.1007/978-3-319-23207-2_25

M3 - Article

SN - 2194-5357

VL - 388

SP - 253

EP - 262

JO - Advances in Intelligent Systems and Computing

JF - Advances in Intelligent Systems and Computing

T2 - The Ninth International Conference on Genetic and Evolutionary Computing (ICGEC 2015)

Y2 - 27 August 2015

ER -

A Communication Strategy for Paralleling Grey Wolf Optimizer

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