Search Results for "ant colony optimization software"
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Showing 16 open source projects for "ant colony optimization software"
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AntTSP
Traveling Salesman Problem (TSP) Solver
Fast Ant Colony Optimization TSP Solver -
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AcoPath for Java
Ant Colony Optimization algorithm for the shortest path problem.
The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of the Ant System is that virtual ants are exploited for finding paths with a specific property, e.g., short distance between physical nodes, in the same way nature guides physical ants. -
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AcoPath
Ant Colony Optimization algorithm for the shortest path problem.
The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of the Ant System is that virtual ants are exploited for finding paths with a specific property, e.g., short distance between physical nodes, in the same way nature guides physical ants. Development takes place at https://github.com/zfoxer/AcoPath -
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MYRA
A collection of ACO algorithms for the data mining classification task
MYRA is a collection of Ant Colony Optimization (ACO) algorithms for the data mining classification task. It includes popular rule induction and decision tree induction algorithms. The algorithms are ready to be used from the command line or can be easily called from your own Java code. They are build using a modular architecture, so they can be easily extended to incorporate different procedures and/or use different parameter values. This project is now hosted at: https://github.com/febo... -
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tsp-problem-ga-aco-comparisson
Genetic Algorithm and Ant Colony to solve the TSP problem
This project compares the classical implementation of Genetic Algorithm and Ant Colony Optimization, to solve a TSP problem. It's possible to define the number of cities to visit , and also interactively create new cities to visit in a 2D spatial panel. A total distance is given for AG and ACO solution at end. -
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Search Agents - a framework for collaboration of meta-heuristic agents
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Swarm Wars
Safety in numbers.
REPOSITORY MOVED TO GITHUB: https://github.com/happyjack27/SwarmWars video sample: http://youtu.be/s5mLNbdBQGY A game where you evolve & compete AI swarms. The organisms use swarm intelligence & ant colony optimization. The organisms can communicate through 3-color signaling as well as by laying beacons. They can attack and repair other organisms. They can select mates, and they can gather and distribute food and material. This behavior is controlled by a genetically evolved... -
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Substitution cipher cracker based on ant colony optimization metaheuristic. Includes GUI. Everything is in czech because this is a school project :(
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ACOptim
Ant Colony Optimizer
Simple POSIX multithread Ant Colony Optimizer framework currently for solving Vehicle Routing Problem. Supports UDP, file synchronizatoin between computers. -
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AntMiner
AntMiner
Inspired by the idea of ant colony optimization (ACO) algorithm being successfully applied to detect epistasis, we introduce an ACO based algorithm, AntMiner, by incorporating heuristic information into ant-decision rules. -
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Implementation of Artificial Inteligence algorithms in python: - Ant Colony Optimization - Greed Algorithm
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Pick-up and Delivery Vehicle Routing Problem with Time Windows and Multiply Types of Googs. Ant Colony optimization approach
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SwarmTSP is a library of swarm intelligence algorithms for the Traveling Salesman Problem. Currently, it implements Ant Colony Optimization and Consultant-Guided Search algorithms.
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<img src=http://www.ifrance.com/divisionX/JOE-LOGO.BMP align=left><div><b>JOE</b> -<i>Java Optimization Engine</i>- provides an easy way to optimize programmatically Java applications. <b>JOE</b> is an Ant optional task (like XDoclet or JavaCC).</div><br
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