Neighbor-Interactive Bee Colony for Problems with Local Structures

Phuc Nguyen HONG; Chang Wook AHN; Jaehoon (Paul) JEONG

doi:10.1587/transfun.E100.A.2034

Neighbor-Interactive Bee Colony for Problems with Local Structures

Phuc Nguyen HONG, Chang Wook AHN, Jaehoon (Paul) JEONG

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In this letter, we integrate domain information into the original artificial bee colony algorithm to create a novel, neighbor-interactive bee colony algorithm. We use the Hamming distance measure to compute variable dependency between two binary variables and employ the Gini correlation coefficient to compute variable relation between integer variables. The proposed optimization method was evaluated by minimizing binary Ising models, integer Potts models, and trapped functions. Experimental results show that the proposed method outperformed the traditional artificial bee colony and other meta-heuristics in all the testing cases.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.9 pp.2034-2037

Publication Date: 2017/09/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E100.A.2034

Type of Manuscript: LETTER

Category: Numerical Analysis and Optimization

Authors

Phuc Nguyen HONG
  Sungkyunkwan University
Chang Wook AHN
  Gwangju Institute of Science and Technology (GIST)
Jaehoon (Paul) JEONG
  Sungkyunkwan University

Keyword

artificial bee colony, Hamming distance, Gini correlation coefficient

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Phuc Nguyen HONG, Chang Wook AHN, Jaehoon (Paul) JEONG, "Neighbor-Interactive Bee Colony for Problems with Local Structures" in IEICE TRANSACTIONS on Fundamentals, vol. E100-A, no. 9, pp. 2034-2037, September 2017, doi: 10.1587/transfun.E100.A.2034.
Abstract: In this letter, we integrate domain information into the original artificial bee colony algorithm to create a novel, neighbor-interactive bee colony algorithm. We use the Hamming distance measure to compute variable dependency between two binary variables and employ the Gini correlation coefficient to compute variable relation between integer variables. The proposed optimization method was evaluated by minimizing binary Ising models, integer Potts models, and trapped functions. Experimental results show that the proposed method outperformed the traditional artificial bee colony and other meta-heuristics in all the testing cases.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.2034/_p

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@ARTICLE{e100-a_9_2034,
author={Phuc Nguyen HONG, Chang Wook AHN, Jaehoon (Paul) JEONG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Neighbor-Interactive Bee Colony for Problems with Local Structures},
year={2017},
volume={E100-A},
number={9},
pages={2034-2037},
abstract={In this letter, we integrate domain information into the original artificial bee colony algorithm to create a novel, neighbor-interactive bee colony algorithm. We use the Hamming distance measure to compute variable dependency between two binary variables and employ the Gini correlation coefficient to compute variable relation between integer variables. The proposed optimization method was evaluated by minimizing binary Ising models, integer Potts models, and trapped functions. Experimental results show that the proposed method outperformed the traditional artificial bee colony and other meta-heuristics in all the testing cases.},
keywords={},
doi={10.1587/transfun.E100.A.2034},
ISSN={1745-1337},
month={September},}

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TY - JOUR
TI - Neighbor-Interactive Bee Colony for Problems with Local Structures
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2034
EP - 2037
AU - Phuc Nguyen HONG
AU - Chang Wook AHN
AU - Jaehoon (Paul) JEONG
PY - 2017
DO - 10.1587/transfun.E100.A.2034
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2017
AB - In this letter, we integrate domain information into the original artificial bee colony algorithm to create a novel, neighbor-interactive bee colony algorithm. We use the Hamming distance measure to compute variable dependency between two binary variables and employ the Gini correlation coefficient to compute variable relation between integer variables. The proposed optimization method was evaluated by minimizing binary Ising models, integer Potts models, and trapped functions. Experimental results show that the proposed method outperformed the traditional artificial bee colony and other meta-heuristics in all the testing cases.
ER -