ant colony optimization
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bijithjos
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09-07-2010, 09:51 PM


please forward me full seminar and presentation report
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project report helper
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18-09-2010, 04:48 PM

More Info About ant colony optimization


The ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.

This algorithm is a member of ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants.

refered by Wikipedia, the free encyclopedia
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arya2011
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05-07-2011, 09:31 PM

please send the full project and implimentation report and ppt
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11-08-2011, 12:12 PM

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11-08-2011, 12:17 PM

Many people not go through do-follow links and they just link to no-follow websites, this is not way of SEO, If you want result in high ranking then you have to follow some rules in ranking part because it is not very easy to search every urls in your keywords base and do-follow and no-follow, these process have lots of efforts so just do it
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seminar addict
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#6
07-02-2012, 03:00 PM

ant colony optimization


.docx   my_seminar.docx (Size: 894.21 KB / Downloads: 38)

INTRODUCTION
1.1 BACK GROUND AND RELATED WORK.
Genetic Algorithms (GA) have been used to evolve computer programs for specific tasks, and to design other computational structures. The recent resurgence of interest in AP with GA has been spurred by the work on Genetic Programming (GP). GP paradigm provides a way to do program induction by searching the space of possible computer programs for an individual computer program that is highly fit in solving or approximately solving the problem at hand. The genetic programming paradigm permits the evolution of computer programs which can perform alternative computations conditioned on the outcome of intermediate calculations, which can perform computations on variables of many different types, which can perform iterations and recursions to achieve the desired result, which can define and subsequently use computed values and subprograms, and whose size, shape, and complexity is not specified in advance. GP use relatively low-level primitives, which are defined separately rather than combined a priori into high-level primitives, since such mechanism generate hierarchical structures that would facilitate the creation of new high-level primitives from built-in low-level primitives. Unfortunately, since every real life problem are dynamic problem, thus their behaviors are much complex, GP suffers from serious weaknesses. Random systems chaos is important, in part, because it helps us to cope with unstable system by improving our ability to describe, to understand, perhaps even to forecast them.


GENETIC PROGRMMING.
Some specific advantages of genetic programming are that no analytical knowledge is needed and still could get accurate results. GP approach does scale with the problem size. GP does impose restrictions on how the structure of solutions should be formulated. There are several variants of GP, some of them are: Linear Genetic Programming (LGP), Gene Expression Programming (GEP), Multi Expression Programming (MEP), Cartesian Genetic Programming (CGP), Traceless Genetic Programming (TGP) and Genetic Algorithm for Deriving Software (GADS).Cartesian Genetic Programming was originally developed by Miller and Thomson for the purpose of evolving digital circuits and represents a program as a directed graph. One of the benefits of this type of representation is the implicit re-use of nodes in the directed graph.



SWARM INTELLIGENCE
Swarm intelligence (SI) describes the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.



ANT COLONY
The complex social behaviours of ants have been much studied by science, and computer scientists are now finding that these behaviour patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behaviour, 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 behaviour. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.

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project girl
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#7
20-12-2012, 06:47 PM

Ant Colony Optimization



.ppt   1Ant Colony.ppt (Size: 1.48 MB / Downloads: 33)

Ant

An ant is a natural creature . They are blind , the ants follow each other by a chemical called pheromone.

introduction

The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers . An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony, In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. 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.

Background

Discrete optimization problems difficult to solve
“Soft computing techniques” developed in past ten years:
Genetic algorithms (GAs)
based on natural selection and genetics
Ant Colony Optimization (ACO)
modeling ant colony behavior
Optimization Technique Proposed by Marco Dorigo in the early ’90
Often applied to TSP (Travelling Salesman Problem): shortest path between n nodes
Artificial ant colony system is made from the principle of ant colony system for solving kinds of optimization problems. Pheromone is the key of the decision-making of ants.

Natural ants: How do they do it?

The pheromone concentration on trail B will increase at a higher rate than on A, and soon the ants on route A will choose to follow route B
Since most ants will no longer travel on route A, and since the pheromone is volatile, trail A will start evaporating
Only the shortest route will remain!

ACO System

Starting node selected at random
Path selected at random
based on amount of “trail” present on possible paths from starting node
higher probability for paths with more “trail”
Ant reaches next node, selects next path
Continues until reaches starting node
Finished “tour” is a solution

conclusion

In nut shell an ant colony concept is used in solving the graph problem because an ant is a natural creature. They use natural mechanism to find the shortest path and Destination(food).
Like this in a computational concept the humans use ant concept for finding the shortest path from source to node. Ant system is also used in many applications that are used in computer and real life problems.
Ants have natural behavior but humans use natural phenomena as well as artificial phenomena in intelligence system
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#8
08-05-2013, 12:42 PM

Ant Colony Optimization


.docx   Ant Colony Optimization.docx (Size: 335.62 KB / Downloads: 21)

INTRODUCTION

In this month’s column I present C# code that implements an Ant Colony Optimization (ACO) algorithm to solve the Traveling Salesman Problem (TSP). An ACO algorithm is an artificial intelligence technique based on the pheromone-laying behavior of ants; it can be used to find solutions to exceedingly complex problems that seek the optimal path through a graph. The best way to see where I’m headed is to take a look at the screenshot in Figure 1. In this case, the demo is solving an instance of the TSP with the goal of finding the shortest path that visits each of 60 cities exactly once. The demo program uses four ants; each ant represents a potential solution. ACO requires the specification of several parameters such as the pheromone influence factor (alpha) and the pheromone evaporation coefficient (rho), which I’ll explain later. The four ants are initialized to random trails through the 60 cities; after initialization, the best ant has a shortest trail length of 245.0 units. The key idea of ACO is the use of simulated pheromones, which attract ants to better trails through the graph. The main processing loop alternates between updating the ant trails based on the current pheromone values and updating the pheromones based on the new ant trails. After the maximum number of times (1,000) through the main processing loop, the program displays the best trail found and its corresponding length (61.0 units).
The 60-city graph is artificially constructed so that every city is connected to every other city, and the distance between any two cities is a random value between 1.0 and 8.0 arbitrary units (miles, km and so forth). There’s no easy way to solve the TSP. With 60 cities, assuming you can start at any city and go either forward or backward, and that all cities are connected, there are a total of (60 - 1)! / 2 = 69,341,559,272,844,917,868,969,509,860,194,703,172,951,438,386,343,716,270,410,647,470,080,000,000,000,000 possible solutions. Even if you could evaluate 1 billion possible solutions per second, it would take about 2.2 * 1063 years to check them all, which is many times longer than the estimated age of the universe.
ACO is a meta-heuristic, meaning that it’s a general framework that can be used to create a specific algorithm to solve a specific graph path problem. Although ACO was proposed in a 1991 doctoral thesis by M. Dorigo, the first detailed description of the algorithm is generally attributed to a 1996 follow-up paper by M. Dorigo, V. Maniezzo and A. Colorni. Since then, ACO has been widely studied and modified, but, somewhat curiously, very few complete and correct implementations are available online.

Updating the Ants

The key to the ACO algorithm is the process that updates each ant and trail by constructing a new—we hope better—trail based on the pheromone and distance information. Take a look at Figure 3. Suppose we have a small graph with just five cities. In Figure 3 the new trail for an ant is under construction. The trail starts at city 1, then goes to city 3, and the update algorithm is determining the next city. Now suppose the pheromone and distance information is as shown in the image. The first step in determining the next city is constructing an array I’ve called “taueta” (because the original research paper used the Greek letters tau and eta). The taueta value is the value of the pheromone on the edge raised to the alpha power, times one over the distance value raised to the beta power. Recall that alpha and beta are global constants that must be specified. Here I’ll assume that alpha is 3 and beta is 2. The taueta values for city 1 and city 3 aren’t computed because they’re already in the current trail. Notice that larger values of the pheromone increase taueta, but larger distances decrease taueta.

Wrapping Up

Let me emphasize that there are many variations of ACO; the version I’ve presented here is just one of many possible approaches. ACO advocates have applied the algorithm to a wide range of combinatorial optimization problems. Other combinatorial opti¬mization algorithms based on the behavior of natural systems include Simulated Annealing (SA), which I covered last month (msdn.microsoftmagazine/hh708758), and Simulated Bee Colony (SBC), which I covered in my April 2011 column (msdn.microsoftmagazine/gg983491). Each approach has strengths and weaknesses. In my opinion, ACO is best-suited for problems that closely resemble the Traveling Salesman Problem, while SA and SBC are better for more general combinatorial optimization problems, such as scheduling.
ACO, in common with other meta-heuristics based on natural systems, is quite sensitive to your choice of free global parameters—alpha, beta and so on. Although there has been quite a bit of research on ACO parameters, the general consensus is that you must experiment a bit with free parameters to get the best combination of performance and solution quality.
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