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Ant Colony Optimization: An Overview
2002Ant Colony Optimization (ACO) is a class of constructive meta-heuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This tutorial is composed of three parts.
Maniezzo V., Carbonaro A.
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Evolving Ant Colony Optimization
Advances in Complex Systems, 1998Ant Colony Optimization (ACO) is a promising new approach to combinatorial optimization. Here ACO is applied to the traveling salesman problem (TSP). Using a genetic algorithm (GA) to find the best set of parameters, we demonstrate the good performance of ACO in finding good solutions to the TSP.
Hozefa M. Botee, Eric Bonabeau
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Modifying Ant Colony Optimization
2008 IEEE Conference on Soft Computing in Industrial Applications, 2008Ant colony optimization (ACO) allows fast near optimal solutions to be found. It is useful in industrial environments where computational resources and time are limited. Like several search methods, being trapped in local optima within a search space currently is still nontrivial difficulty for ACO.
Sarayut Nonsiri, Siriporn Supratid
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Meeting Ant Colony Optimization
2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, 2008The ant system is a new meta-heuristic mainly for hard combinatorial optimization problems. It has been unexpectedly successful and known as ant colony optimization (ACO) in recent years. Nowadays, a series of improvements have been made to the ACO, most of which focus on the exploitation of gather information to guide the search of ant colony towards ...
Zhang Fei Jun, Gao Wei
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2012 International Conference on Computer Science and Electronics Engineering, 2012
Recently, the application of Ant Colony Optimization is much wider and it is always the highlight of key algorithm. There are also many improvements about the ACO algorithm, such as: the improvements of algorithm in self-adaptive, the improvements of increasing the diversity of various group, the improvements of enhancing local search, combining with ...
Ying Pei, Wenbo Wang, Song Zhang
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Recently, the application of Ant Colony Optimization is much wider and it is always the highlight of key algorithm. There are also many improvements about the ACO algorithm, such as: the improvements of algorithm in self-adaptive, the improvements of increasing the diversity of various group, the improvements of enhancing local search, combining with ...
Ying Pei, Wenbo Wang, Song Zhang
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Pipe Routing through Ant Colony Optimization
Journal of Infrastructure Systems, 2010As the need to better manage scarce water resources and water distribution systems increases, the problem of efficient routing of piping networks is gaining importance within the framework of an overall strategy for improving the networks’ efficiency and resilience to undesired emphoperational changes.
Christodoulou, Symeon E. +3 more
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Ant colony optimization in lens design
Applied Optics, 2019As the most widely used optimization algorithm in optical design, the damped least square (DLS) method is advantageous for its fast convergence and deterministic optimization path. However, results are strongly dependent on the initial system and problematic when it is stuck in a local minimum in the searching space.
Ziyao, Tang +2 more
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Do Ants Use Ant Colony Optimization?
2018Ant Colony Optimization (ACO) is a widespread optimization technique used to solve complex problems in a broad range of fields, including engineering, software development and logistics. It was inspired by the behaviour of ants which can collectively select the shorter of two paths leading to a food source.
Wolfhard von Thienen, Tomer J. Czaczkes
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