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	Ant colony optimization - Wikipedia, the free encyclopedia
		The ant colony optimization algorithm (ACO), introduced by Marco Dorigo in his doctoral thesis in 1992, is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
		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.
	en.wikipedia.org /wiki/Ant_colony_optimization (506 words)

	Ant colony optimization -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-10-08)
		They are inspired by the behavior of (Social insect living in organized colonies; characteristically the males and fertile queen have wings during breeding season; wingless sterile females are the workers) ants in finding paths from the colony to food.
		In the real world, ants (initially) wander (additional info and facts about random) randomly, and when having found food, returning to their colony while laying down (A chemical substance secreted externally by some animals (especially insects) that influences the physiology or behavior of other animals of the same species) pheromone trails.
		Ant colony optimization algorithms have been used to produce near-optimal solutions to the (additional info and facts about traveling salesman problem) traveling salesman problem.
	www.absoluteastronomy.com /encyclopedia/a/an/ant_colony_optimization.htm (295 words)

	Description of the CIAC algorithm [NoJhan - Site perso]
		Ant colony algorithms are a class of metaheuristics which are inspired from the behaviour of real ants.
		The algorithm sends some local ants on regions, these ants lay down some pheromonal spots when they find an improvement of the objective function and the spots are attractive for all the ants of the colony.
		The number of ants $\eta $ is not a critical parameter as it doesn’t influence critically the overall convergence of the algorithm.
	nojhan.free.fr /article.php3?id_article=9 (3756 words)

	Encyclopedia: Ant colony algorithm
		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 graphs.
		In the real world, ants (initially) wander randomly, and when having found food, returning to their colony while laying down pheromone trails.
		If other ants find such a path, they are likely not to travel on at random but to follow the trail, and return and reinforce it if they eventually find food.
	www.nationmaster.com /encyclopedia/Ant-colony-algorithm (276 words)

	Ant colony algorithm - InfoSearchPoint.com (Site not responding. Last check: 2007-10-08)
		The ant colony algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
		Each ant wanders randomly, but is more likely to travel a path that has pheromone on it.
		Thus, when one ant finds a good (short) path from the colony to a food source, other ants are more likely to follow that path; positive feedback eventually leaves all the ants following a single path.
	www.infosearchpoint.com /display/Ant_colony_algorithm (213 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.
		In order to use an ant algorithm most efficiently, the problem needs to be able to be represented as a graph with weighted edges.
		Each time a colony searches for a roommate, the probability of an ant of the colony choosing a particular roommate must be determined.
	www.cs.earlham.edu /%7Euptongl/project/senior_thesis.html (2394 words)

	Ant Colony Algorithms for Routing in Sensor Networks...
		In each pass of the proposed algorithm, ants are placed at the terminal nodes of the tree to be computed.
		In addition to forward and backwork ants, we also use random ants whose purpose is to enable sharing of information pertaining to a node potential with neighboring sensors.
		Since ant algorithms perform computations solely through local interactions between ant-like agents by means of pheromones, they scale well for large-scale applications, and are particularly attractive for real world systems.
	www.cs.bham.ac.uk /~wbl/biblio/gecco2004/prof205.html (215 words)

	Seminar with Eric Bonabeau May99
		Algorithms can be used on a computer generated image to stretch or shrink its dimensions in order to reveal hidden structure.
		There's an ant species in Switzerland which forms what may be regarded as a super colony in that it consists of a number of sub-nests usually with one or several queens.
		Similarly there is an army ant colony in South America which is nomadic and when on the move the front of the swarm forms a tree-like structure called a 'bivouac' which is actually optimal as a food distribution network for the energy expended.
	www.psych.lse.ac.uk /complexity/Seminars/1999/report99may.htm (4588 words)

	BioMed Central \| Full text \| An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding ...
		The contact interactions (CI) algorithm by Toma and Toma [24] combines the idea of HZ with a Monte Carlo search procedure that assigns different conformational freedom to the different residues in the chain, and thus allows previously formed contacts to be modified according to their computed mobilities.
		The run-times for both algorithms are reported in detail in Additional file 1; we note that on some sequences, the performance of PERM depends significantly on the direction of folding.
		Each ant performs probabilistic chain-growth construction of the protein conformation, where in every step, the structure is extended either to the left or to the right, such that the ratio of unfolded residues at each end of the protein remains (roughly) unchanged.
	www.biomedcentral.com /1471-2105/6/30 (8225 words)

	Amazon.com: Ant Colony Optimization (Bradford Books): Books: Marco Dorigo,Thomas Stützle (Site not responding. Last check: 2007-10-08)
		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.
		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.
	www.amazon.com /exec/obidos/tg/detail/-/0262042193?v=glance (1292 words)

	An introduction to Ant Colony Algorithms: The Algorithms (Site not responding. Last check: 2007-10-08)
		Ant Colony Algorithms are inspired by the behaviour of natural ant colonies, in the sense that they solve their problems by multi agent cooperation using indirect communication through modifications in the environment.
		This simple behaviour explains why ants are able to adjust to changes in the environment, such as new obstacles interrupting the currently shortest path.
		The ants use information collected during past simulations to direct their search and this information is available and modified through the environment.
	www.cogs.susx.ac.uk /lab/nlp/gazdar/teach/atc/1999/web/johannf/ants.html (341 words)

	News from
		An algorithm is a set of instructions used by the computer to accomplish its tasks.
		Ants succeed in nature, however, because of the collective strategy they use and their ability to communicate with each other.
		He and Don Gruenbacher, associate professor of electrical and computer engineering, are using the ant colony algorithm in the creation of adaptable computer hardware.
	www.engg.ksu.edu /news/092002.html (745 words)

	An introduction to Ant Colony Algorithms (Site not responding. Last check: 2007-10-08)
		Real ants are capable of finding the shortest path from their nest to a food source without visual sensing.
		Ant Colony Algorithms try to make use of these real ant abilities to solve various optimization problems.
		After introducing the basic idea behind the algorithms we take a look at how an Ant Colony Algorithm can be used to solve the Travelling Salesman Problem.
	www.informatics.sussex.ac.uk /research/nlp/gazdar/teach/atc/1999/web/johannf (110 words)

	An introduction to Ant Colony Algorithms: Travelling Salesman (Site not responding. Last check: 2007-10-08)
		For example each ant is able to determine how far away cities are, and they all have a memory of which cities they have already visited.
		The algorithm was applied to both symmetric and asymmetric versions of the TSP problem and its performance was tested against several other naturally inspired global optimization methods, such as neural nets, genetic algorithms and simulated annealing.
		The ant algorithm usually found equally good or better solutions than the other methods, and in the remaining cases it was outperformed by a very narrow margin.
	www.informatics.sussex.ac.uk /research/nlp/gazdar/teach/atc/1999/web/johannf/tsp.html (575 words)

	An Ant Colony Algorithm for Classification Rule Discovery - Parpinelli, Lopes, Freitas (ResearchIndex) (Site not responding. Last check: 2007-10-08)
		An Ant Colony Algorithm for Classification Rule Discovery (2002)
		An Ant Colony Algorithm for Classification Rule Discovery.
		76 Ant algorithms for discrete optimization - Dorigo, Di Caro et al.
	citeseer.ist.psu.edu /458683.html (491 words)

	Session: Ant Colony Methods – 1 Session (Site not responding. Last check: 2007-10-08)
		An ACO algorithm is based on the result of low-level interaction among many cooperating simple agents that are not explicitly aware of their cooperative behavior.
		Each simple agent is called ant and the ACO algorithm (a distributed algorithm) is based on a set of ants working independently and cooperating sporadically in a common problem solving activity.
		Our method is based on a process in which an ant traverses the graph by moving from vertex to vertex along the edges, occasionally leaving traces in the vertices, and deciding on the next step according to the level of traces in the surrounding neighborhood.
	garage.cps.msu.edu /cec99/specialSessions/Dorigo.html (587 words)

	ant colony -- ant colony (Site not responding. Last check: 2007-10-08)
		Ant Colony Optimization Algorithm S c i e n c e "Ants are a classic example of social insects, which work together for the good of the colony.
		Ant Colony Activity Name Ant Colony Aim Students to gain an understanding of how seeds are transported, sometimes long distances, by ants, and the implications of this behaviour for seed dispersal...
		Ant Colony Optimization: Models and Applications Ant Colony Optimization (ACO) is a recent metaheuristic method to solve combinatorial optimization problems that is inspired by the shortest path...
	www.dcants.com /antcolony (3571 words)

	Evolving Ant Colony Optimization - Botee, Bonabeau (ResearchIndex) (Site not responding. Last check: 2007-10-08)
		Ant Colony Optimization (ACO) is a promising new approach to combinatorial optimization.
		Using a genetic algorithm (GA) to nd the best set of parameters, we demonstrate the good performance of ACO in nding good solutions to the TSP.
		2 Ant colonies for the QAP J (context) - Gambardella, Taillard et al.
	citeseer.lcs.mit.edu /43988.html (559 words)

	Computer Science: Applied and Interdisciplinary Informatics Group publications
		A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data.
		Comparing a genetic algorithm with a rule induction algorithm in the data mining task of dependence modeling.
		A comparison of the performances of a bayesian algorithm and a kohonen map for clustering texture data.
	www.cs.kent.ac.uk /research/groups/aii/pubs.html (9179 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.
		The ant-cycle algorithm uses alpha = 1.0, beta = 2.0, rho = 0.5, an initial tau (pheromone) on each link of 0.1, and a Q of 100.
	uk.geocities.com /markcsinclair/aco.html (360 words)

	Sitereview.org: Ant Colony Optimization Algorithm (Site not responding. Last check: 2007-10-08)
		"Ants are a classic example of social insects, which work together for the good of the colony.
		A colony of ants finds new food sources by sending out foragers who explore the surroundings more or less at random.
		If it finds food, a forager will return to the colony, laying a pheromone trail as it goes - a trail that other ants can follow back to the food.
	sitereview.org /?article=364 (174 words)

	jls
		The ant colony optimisation algorithm was developed by Dorigo about 10 years ago.
		The ant colony algorithm basically has a population of agents (ants) which are each carrying out approximately greedy search.
		This approach is related to genetic algorithms, where mutation and crossover are replaced by sampling from the distribution.
	www.cs.manchester.ac.uk /mscprojects/projects.04/jls.html (821 words)

	Glen's Senior Project (Site not responding. Last check: 2007-10-08)
		In swarm applications, the units working on the problem usually have no knowledge that a problem even exists, they are in fact just continuing with their natural behavior, and it is that behavior that helps solve the problem.
		For example: ant colonies have a natural ability to find the shortest path from one route to another, while avoid obstacles and danger.
		Each roommate has a colony of ants, and the ants go out in search of other roommates who are similar to themselves.
	www.cs.earlham.edu /~uptongl/project (391 words)

	INFORMS San Antonio 2000 Session TC33 (Site not responding. Last check: 2007-10-08)
		We present a B&B algorithm for the problem of minimizing the makespan on identical parallel machines subject to release dates and delivery times.
		A preprocessing algorithm and a new tight lower bound are introduced.
		Also, the search tree is reduced using a polynomial selective algorithm.
	www.informs.org /Conf/SanAntonio2000/TALKS/TC33.html (280 words)

	Ant colony optimization - Indopedia, the Indological knowledgebase
		Ant colony optimization - Indopedia, the Indological knowledgebase
		(Details on this behaviour.) Thus, when one ant finds a good (i.
		short) 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.indopedia.org /ACO.html (325 words)

	Stochastic Process Modeling
		The Ant Colony algorithm, embodied by the concept of swarm intelligence, is interesting because like real ants, there is no central control or command.
		This talk will provide an overview of Swarm Intelligence, describe the mechanisms for constructing an Ant Colony algorithm and provide practical applications and resources for further research.
		Djang, Philipp A., An Ant Colony OptimizationApproach to the Border Penetration Model, December 2002.
	engr.nmsu.edu /~csm/seminar.htm (1155 words)

	Luca Gambardella
		Ant Colony Optimization algorithms able to compute best-known solutions for many benchmark instances: Sequential ordering problems (SOP), Vehicle routing problems (VRP), Travelling salesman problems (TSP), Quadratic assignment problems (QAP).
		The main scientific objective of BISON is to explore the use Ant Colony Optimization and Immune Networks for routing and optimization in Ad-Hoc networks and Grid computing systems.
		A Swiss National Science Foundation project of two years, consisting in analyzing and extend ACS (ant colony system) a novel approach to combinatorial optimization based on the cooperation of a colony of agents.
	www.idsia.ch /~luca (4982 words)

	Ant Colony Programming for Approximation (ResearchIndex) (Site not responding. Last check: 2007-10-08)
		We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program.
		The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.
		150 Ant Colony System: A cooperative learning approach to the Tr..
	citeseer.ist.psu.edu /632253.html (403 words)

	An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem (ResearchIndex) (Site not responding. Last check: 2007-10-08)
		An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem
		We present an improved version of our recently proposed Ant Colony Optimisation (ACO) algorithm for this -hard combinatorial problem and demonstrate its ability to solve standard benchmark instances...
		1 An Ant Colony Algorithm for the 2D HP Protein Folding Proble..
	citeseer.ist.psu.edu /575005.html (448 words)

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