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Ant colony optimization theory: A survey [PDF]
Theoretical Computer Science, 2005 Research on a new metaheuristic for optimization is often initially focused on proof-of-concept applications. It is only after experimental work has shown the practical interest of the method that researchers try to deepen their understanding of the method's functioning not only through more and more sophisticated experiments but also by means of an ...
Christian Blum, Marco Dorigo
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Test pattern optimization scheme based on Hybrid Ant Colony Optimization [PDF]
Scientific ReportsThe trend toward device miniaturization has made digital circuit testing both essential and increasingly complex. To achieve complete fault coverage, a large number of test patterns are applied, which leads to increased switching activity due to larger ...
S. Asha Pon, V. Jeyalakshmi
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Ant colony optimization: A bibliometric review
Physics of Life ReviewsThis paper is a follow-up of one of the most-cited articles published in the first 20 years of the existence of Physics of Life Reviews. The specific topic is "ant colony optimization", which is a metaheuristic for solving challenging optimization problems.
Christian Blum
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Path planning of multiple UAVs using MMACO and DE algorithm in dynamic environment
Measurement + Control, 2023 Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue.
Zain Anwar Ali, Han Zhangang, Di Zhengru
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Service Composition and Optimization Method Based on Improved Ant Colony Optimization Algorithm [PDF]
Jisuanji gongcheng, 2018 Aiming at the shortcomings of traditional ant colony algorithm,such as long initial pheromone accumulation time and easy to fall into local optimum,an improved ant colony system based cloud service composition algorithm is proposed under the condition of
SHEN Jiquan,LUO Changwei,HOU Zhanwei,LIU Zhizhong
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Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2009 Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to ...
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Dynamic Path Optimization Based on Improved Ant Colony Algorithm
Journal of Advanced Transportation, 2023 Dynamic path optimization is an important part of intelligent transportation systems (ITSs). Aiming at the shortcomings of the current dynamic path optimization method, the improved ant colony algorithm was used to optimize the dynamic path.
Juan Cheng
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Single-Colony Adaptive Heterogeneous Ant Colony Algorithm for Mobile Robot Path Planning [PDF]
Jisuanji kexue yu tansuo, 2022 Aiming at the existent problems of complex structure, slow optimization rate and inadequate adaptability of multi-colony ant colony algorithm, a single colony adaptive heterogeneous ant colony algorithm is proposed to solve mobile robot path planning ...
ZHANG Songcan, SUN Lifan, SI Yanna, PU Jiexin
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Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, 2010 Swarm intelligence is a relatively novel approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful one is the ant colony optimization.
Benlian Xu, Jihong Zhu, Qinlan Che
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On the Invariance of Ant Colony Optimization [PDF]
IEEE Transactions on Evolutionary Computation, 2007 Ant colony optimization (ACO) is a promising metaheuristic and a great amount of research has been devoted to its empirical and theoretical analysis. Recently, with the introduction of the hypercube framework, Blum and Dorigo have explicitly raised the issue of the invariance of ACO algorithms to transformation of units.
Mauro Birattari +2 more
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