Search Results for "artificial bee colony java"

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.

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/myra

GUI Ant-Miner is a tool for extracting classification rules from data. It is an updated version of a data mining algorithm called Ant-Miner (Ant Colony-based Data Miner).

This is a GUI, Java implementation of the Ant Colony Optimisation/Particle Swarm Optimisation (PSO/ACO2) rule induction algorithm. This project was inspired by Ant-Miner, but handles continuous attributes using PSO or now Differential Evolution.

Search Agents - a framework for collaboration of meta-heuristic agents

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...

SwarmTSP is a library of swarm intelligence algorithms for the Traveling Salesman Problem. Currently, it implements Ant Colony Optimization and Consultant-Guided Search algorithms.

The implementation of Bee Hive @ Work algorithm that simulates the foraging behavior of honey bees in nature. The aim is to provide an extensible framework that can be used by researchers to simply create new applications of this algorithm.

Distributed Ant Robotics is divided into two parts. First is the simulation of “ant colony”. Second part is the algorithm application. Where you can use algorithms for ant behavior.

TACS (Trust Ant Colony System) is a Trust model for P2P, Ad-hoc and Wireless Sensor networks (also valid for multi-agent systems) based on the bio-inspired algorithm ACS (Ant Colony System).

Investigate and visualize ant-routing (or, Ant Colony Optimization)