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A bare-bones ant colony optimization algorithm that performs competitively on the sequential ordering problem

delete2014-01-14
delete6
PRE
AI
A
Ahmed Ezzat
A
Ashraf M. Abdelbar *
D
Donald C. Wunsch
DOI:10.1007/s12293-013-0129-zdelete
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摘要

摘要

En 中文
EigenAnt is a bare-bones ant colony optimization algorithm that has been proven to converge to the optimal solution under certain conditions. In this paper, we extend EigenAnt to the sequential ordering problem (SOP), comparing its performance to Gambardella et al.'s enhanced ant colony system (EACS), a model that has been found to have state-of-the-art performance on the SOP. Our experimental results, using the SOPLIB2006 instance library, indicate that there is no statistically significant difference in performance between our proposed method and the state-of-the-art EACS method.
Keyword:
Ant colony optimization
Sequential ordering problem
Swarm intelligence
EigenAnt algorithm
Ant colony system
Metaheuristic
Traveling salesman problem

期刊

Memetic Computing 封面图
Memetic Computing
IF:
2.3
论文数:
453
被引数:
718

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A
American University Cairo
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1.0K
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E
egyptian knowledge bank (ekb)
学者数:
11.6W
论文数: 9.3W
被引数: 84
B
brandon university
学者数:
443
论文数: 567
被引数: 8
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