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Topic: Ant colony optimization

	Ant Colony Optimization: An Overview - Maniezzo, Carbonaro (ResearchIndex)
		Ant Colony Optimization (ACO) is a class of constructive metaheuristic 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.
		The rst one frames the ACO approach in current trends of research on metaheuristic algorithms for combinatorial optimization; the second outlines current research within the ACO framework,...
		Islands of colonies are used in the case of...
	citeseer.ist.psu.edu /197565.html (982 words)

	An Ant Colony Optimization Approach to the Probabilistic Traveling Salesman Problem - Bianchi, Gambardella, Dorigo ...
		The goal is to find an a priori tour of minimal expected length over all customers, with the strategy of visiting a random subset of customers in the same order as they appear in the a priori tour.
		An ant colony optimization approach to the probabilistic traveling salesman problem.
		Ant Colony Optimization for FOP Shop Scheduling: A case study..
	citeseer.ist.psu.edu /bianchi02ant.html (499 words)

	Metaheuristics Network
		Ant Colony Optimization (ACO) is a metaheuristic approach proposed by Dorigo et al.
		Ants move by applying a stochastic local decision policy that makes use of the pheromone values and the heuristic values on components and/or connections of the construction graph.
		While moving, the ant keeps in memory the partial solution it has built in terms of the path it was walking on the construction graph.
	www.metaheuristics.net /index.php?main=3 (765 words)

	Ant colony optimization (Site not responding. Last check: )
		The ant colony optimization algorithm (ACO), introduced by Marco Dorigo in his doctoral thesis, is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphss.
		The idea of the ant colony algorithm is to mimic this behavior with "simulated ants" walking around the graph representing the problem to solve.
		Ant colony optimization algorithms have been used to produce near-optimal solutions to the traveling salesman problem.
	www.xasa.com /wiki/en/wikipedia/a/an/ant_colony_optimization.html (238 words)

	Ant colony optimization in TutorGig Encyclopedia
		The 'ant colony optimization' algorithm (ACO), introduced by Marco Dorigo Dor92,DoSt04 and by Moyson and Manderick MoMa88, is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
		If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food (see Ant communication and behavior).
		Thus, when one ant finds a good (short, in other words) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leaves all the ants following a single path.
	www.tutorgig.com /ed/ant_colony_optimization (812 words)

	Optimization Online - Reservoir Operation by Ant Colony Optimization Algorithms
		To apply ACO algorithms, the problem is approached by considering a finite horizon with a time series of inflow, classifying the reservoir volume to several intervals, and deciding for releases at each period with respect to a predefined optimality criterion.
		Three alternative formulations of ACO algorithms for reservoir operation are presented using a single reservoir, deterministic, finite-horizon problem and applied to the Dez reservoir in Iran.
		It is concluded that the ant colony system global-best algorithm provides better and comparable results with known global optimum results.
	www.optimization-online.org /DB_HTML/2003/07/696.html (190 words)

	Ant Colony Optimization - Scholarpedia
		Pheromone values are modified at runtime and represent the cumulated experience of the ant colony, while heuristic values are problem dependent values that, in the case of the TSP, are set to be the inverse of the lengths of the edges.
		Current research in ACO algorithms is devoted both to the development of theoretical foundations and to the application of the metaheuristic to new challenging problems.
		These results can be explained by the fact that, while moving, ants deposit pheromone on the ground; and whenever they must choose which path to follow, their choice is biased by pheromone: the higher the pheromone concentration found on a particular path, the higher is the probability to follow that path.
	www.scholarpedia.org /wiki/index.php?title=Ant_Colony_Optimization (0 words)

	Ants Can Successfully Design GPS Surveying Networks - GPS World
		The ACS algorithm is inspired by the foraging behavior of ants, which can find short paths from their nest to food sources by laying down pheromone traces on the ground.
		Foraging ants mark their path by laying down chemical cues called pheromones, varying the amount of chemical deposited depending on the quantity of food and its distance from the nest.
		Ants that discover the shortest route are able to move back and forth between their nest and the food source, depositing higher levels of pheromone as they do so.
	www.gpsworld.com /gpsworld/article/articleDetail.jsp?id=30690&pageID=1 (913 words)

	Ants 2006: Fifth International Workshop on Ant Colony Optimization and Swarm Intelligence
		An example of a particularly successful research direction in swarm intelligence is ant colony optimization, the main focus of which is on discrete optimization problems.
		Ant colony optimization has been applied successfully to a large number of difficult discrete optimization problems including the traveling salesman problem, the quadratic assignment problem, scheduling, vehicle routing, etc., as well as to routing in telecommunication networks.
		The ANTS 2006 workshop will give researchers in swarm intelligence the opportunity to meet, to present their latest research, and to discuss current developments and applications.
	iridia.ulb.ac.be /~ants/ants2006 (0 words)

	Ant colony optimization - Definition, explanation
		A virtual ant colony was created to investigate the emergent behaviors demonstrated...
		Contains a report on Ant Routing System, which is a routing algorithm based on ant algorithms applied to a simulated network.
		Studies artificial systems that take inspiration from the behavior of real ant colonies and which are used to solve discrete optimization problems.
	www.calsky.com /lexikon/en/txt/a/an/ant_colony_optimization.php (499 words)

	LavaCUBED \Computers\Artificial_Life\Ant_Colony_Optimization (Site not responding. Last check: )
		Ant Colony Optimization - Optimization methodology based on ant behaviors.
		ANTS Workshop Series - From Ant Colonies to Artificial Ants: A Series of International Workshops on Ant Algorithms.
		Simulation of Ant's Emergent Behavior Using StarLogo - A virtual ant colony was created to investigate the emergent behaviors demonstrated by ants.
	www.lavacubed.com /new.cats.php?path=/Computers/Artificial_Life/Ant_Colony_Optimization (98 words)

	Ant-Colony Optimization Algorithm For Intrusion Detection
		Ant-colony optimization algorithm is an evolutionary learning algorithm which could be applied to solve the combinatorial optimization problems [9]-[11].
		Ant-Miner [19] is an ant colony based system which is used for the classification task of data mining.
		Number of rules used to test convergence of the ants (No_ rules_ converge): If the current ant has constructed a rule that is exactly the same as the rules constructed by the No_ rules_ converge -1 previous ants, then convergence has occurred.
	www.allconferences.com /conferences/20060602203321 (2414 words)

	An Ant Colony Optimization Algorithm for the Stable Roommates Problem
		The ants that find the closest food source will be able to leave more amounts of pheromone since their travel time is less then the other ants.
		Each time a colony searches for a roommate, the probability of an ant of the colony choosing a particular roommate must be determined.
		It is possible that a colony will converge on a sub-optimal solution, and so a rogue ant must be sent to explore the graph periodically.
	www.cs.earlham.edu /~uptongl/project/senior_thesis.html (2394 words)

	AntOptima SA - solutions - ant colony optimization
		The ant colony optimization (ACO) metaheuristic is a population-based approach to the solution of combinatorial optimization problems.
		The basic ACO idea is that a large number of simple artificial agents are able to build good solutions to hard combinatorial optimization problems via low-level based communications.
		Artificial ants are implemented as parallel processes whose role is to build problem solutions using a constructive procedure driven by a combination of artificial pheromone, problem data and a heuristic function used to evaluate successive constructive steps.
	www.antoptima.com /site/en/solutions/ant.html (210 words)

	Ant Colony Optimization Theory: A Survey (Site not responding. Last check: )
		Excerpt: 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 effort to build a theory.
		Ant colony optimization, which was introduced in the early 1990s as a novel technique for solving hard combinatorial optimization problems, finds itself currently at this point of its life cycle.
	www.comdig.org /print_article.php?id_article=22447 (160 words)

	Evolving Ant Colony Optimization
		In particular, several ant species are capable of selecting the shortest pathway, among a set of alternative pathways, from their nest to a food source (Beckers et al., 1990).
		Ants deploy a chemical trail (or pheromone trail) as they walk; this trail attracts other ants to take the path that has the most pheromone.
		In the present letter we demonstrate the good performance of ACO algorithms when parameters are selected using a systematic procedure.
	www.santafe.edu /research/publications/wpabstract/199901009 (251 words)

	Ant Colony Optimisation
		Ant Colony Optimisation is a new class of natural algorithms inspired by the foraging behaviour of natural ant colonies.
		Ant Colony Optimization Web page, or read the introductory sections in a recent paper of mine, Ant Colony Optimisation for Virtual-Wavelength-Path Routing and Wavelength Allocation.
		Once the ACO has been started by pressing 'Run', then after each full cycle the links (routes between cities) are displayed coloured by their relative pheromone strength, from the lowest, white (invisible) through dark gray, fl, blue, cyan, green, yellow, orange, orange-red to the most attractive, red.
	uk.geocities.com /markcsinclair/aco.html (360 words)

	Ant Colony Optimization - The MIT Press
		The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization.
		Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
		Marco Dorigo is research director of the IRIDIA lab at the Université Libre de Bruxelles and the inventor of the Ant Colony Optimization metaheuristic for combinatorial optimization problems.
	mitpress.mit.edu /0262042193 (0 words)

	Artificial intelligence network load balancing using Ant Colony Optimisation - The Code Project - C# Algorithms
		Ants first evolved around 120 million years ago, take form in over 11,400 different species and are considered one of the most successful insects due to their highly organised colonies, sometimes consisting of millions of ants.
		The result of these studies is Ant Colony Optimisation (ACO) and in the case of well implemented ACO techniques, optimal performance is comparative to existing top-performing routing algorithms.
		An ant is placed on a network of 4 nodes with the source node of 1 and destination node 2.
	www.codeproject.com /useritems/Ant_Colony_Optimisation.asp (2488 words)

	> Computers> Artificial Life> Ant Colony Optimization
		Ant Colony Optimization - - Optimization methodology based on ant behaviors.
		ANTS Workshop Series - - From Ant Colonies to Artificial Ants: A Series of International Workshops on Ant Algorithms.
		Simulation of Ant's Emergent Behavior Using StarLogo - - A virtual ant colony was created to investigate the emergent behaviors demonstrated by ants.
	www.wizzle.co.uk /dir/Computers/Artificial_Life/Ant_Colony_Optimization (107 words)

	Dungeon7 Sciences
		Ant Colony Optimization) is a metaheuristic where a colony of digital ants searches for optimal solutions to complex multidimensional problems.
		Ants coordinate their activities via indirect communication; for example, a foraging ant that has found food will deposit a chemical on the ground (pheromones) that increases the probability that other ants in the colony will follow its same path.
		Pheromones build up with each passing ant, and those paths/steps with the highest pheromone values are most likely to be chosen by the next ant.
	www.d7s.com /acostrawberry.htm (517 words)

	Amazon.ca: Ant Colony Optimization: Books: Marco Dorigo,Thomas Ste (Site not responding. Last check: )
		Ant Colony Optimization presents the most successful algorithmic techniques to be developed on the basis on ant behavior.
		The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior.
		Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
	www.amazon.ca /Ant-Colony-Optimization-Marco-Dorigo/dp/0262042193 (607 words)

	Luca Gambardella: Ant Colony Optimization
		The ant colony optimization metaheuristic (ACO, Dorigo, Di Caro and Gambardella 1999) is a population-based approach to the solution of combinatorial optimization problems.
		The basic ACO idea is that a large number of simple artificial agents are able to build good solutions to hard combinatorial optimization problems via low-level based communications.
		Artificial ants are implemented as parallel processes whose role is to build problem solutions using a constructive procedure driven by a combination of artificial pheromone, problem data and a heuristic function used to evaluate successive constructive steps).
	www.idsia.ch /~luca/antcolonyoptimization.html (441 words)

	Amazon.com: Ant Colony Optimization (Bradford Books): Books: Marco Dorigo,Thomas Stützle (Site not responding. Last check: )
		That is the basis of ant colony optimization.
		Dorigo, the principal author and founder of the ant school, uses this chapter to express his pure joy at having found such a wonderful thing, and at the similar approaches that others have also found.
		The initial idea of ACO may be bio-inspired, but this book has a crystal clear focus of the computational considerations in optimization theory.
	www.amazon.com /Ant-Colony-Optimization-Bradford-Books/dp/0262042193 (1770 words)

	Working Paper - Applying ant colony optimization to solve the single machine total tardiness problem (by Bauer, ... (Site not responding. Last check: )
		Ant Colony Optimization is a relatively new meta-heuristic that has proven its quality and versatility on various combinatorial optimization problems such as the traveling salesman problem, the vehicle routing problem and the job shop scheduling problem.
		The paper introduces an Ant Colony Optimization approach to solve the problem of determining a job-sequence that minimizes the overall tardiness for a given set of jobs to be processed on a single, continuously available machine, the Single Machine Total Tardiness Problem.
		Experiments with 250 benchmark problems with 50 and 100 jobs illustrate that Ant Colony Optimization is an adequate method to tackle the SMTTP.
	epub.wu-wien.ac.at /dyn/virlib/wp/showentry?ID=epub-wu-01_1dd (218 words)

	Ant Colony Optimization Authorship - Dreaming of Metaheuristics
		It is due to the fact that researchers want to share free science with everybody (at least at little cost), and that recognition is a form of remuneration (in a similar way, Eric S. Raymond explain such mechanism for hackers, in his essay "The Cathedral and the Bazaar").
		The 15th of august, Daniel Angus posted an email on the ACO mailing-list (and a post on his laboratory blog), asking "why this paper has not received more attention from the field?".
		The new methodology and the emergence of self-organization are illustrated by the adaptive response of ant colonies to their environment.
	nojhan.free.fr /metah/index.php?2006/08/26/6-ant-colony-optimization-authorship (1024 words)

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