by Anand Veeraswamy
Project supervisor: Bala Amavasai
Microsystems and Machine Vision Laboratory
Sheffield Hallam University
The applet requires Java 1.4.1 or higher. It will not run on Windows 95 or Mac OS 8 or 9. Mac users must have OS X 10.2.6 or higher and use a browser that supports Java 1.4. (Safari works, IE does not. Mac OS X comes with Safari. Open Safari and set it as your default web browser under Safari/Preferences/General.) On other operating systems, you may obtain the latest Java plugin from Sun's Java site.
Please scroll down for instructionsview/download model file: path-forage.v1.1.nlogo
This work has been published in the paper entitled "Optimal Path Planning Applied to Ant Foraging". It takes a hybridised approach by combining a biologically evolved technique such as Ant Foraging with a mathematically evolved search technique called the A* algorithm.
This is not an attempt to reproduce the exact behaviour of ant colonies. We are more interesting in improving biologically evolved techniques to create more efficient robots.
In this experiment there are 2 kinds of breeds. One is simply the 'Ant' breed and the other is the 'Path-Finding-Ant' breed. The Ants go about their foraging with the help of two pheromone types, i.e. food pheromones and nest pheromones to create trails of food and nest respectively. The Path-Finding-Ant goes about its task which is to use the A* algorithm to find an optimal path between nest and food. Once it has been found the path, it continuously traverses between the food and the nest dropping the appropriate pheromones. The Ant breed also drop pheromones. But since the Path-Finding-Ant is more efficient it finds the path more quickly and this helps the other ants to find the path quickly thereby increasing the efficiency of foraging.
*Note: The path finding ant cannot be seen in the simulations because it moves much faster than the other ants and updating the display to make it visible slows down the simulation considerably.
Click the setup button to reset the simulation. Click on scenario to change the scenarios. Set the 'path-finding-ant-number' to 0 if you want to see the foraging process without path finding. Setting it to 1 will enable to see the foraging process with path finding. 'target-change-rate' specifies the number of simulation time steps at which the food source and nest change positions.
Click the GO button to start the simulation. The ants are initially colored red. Once the Ants find food their color changes to brown. If an ant is sucessful in dropping the food at the nest its color changes to pink.
Changing the "Ant Number" increases or decreases the number of ants in the simulation.
Use the Evaporation slider and diffusion slider to change the rate of evaporation and diffusion. The present model seems to work better at lower rates of diffusion. The conformance slider should be ideally set to 0.95 which encourages the ants to explore.
With the Number of Path finding ants turned to zero, there is no path finding taking place which allows the ants to find a path using purely home and nest pheromones. The changes in the rate of evaporation and diffusion can be noticed here. But with path finding enabled(Setting Path-Finding-Ants-Number to 1) the rated of diffusion and evaporation do not actually make much of a difference.
NetLogo A* implementation Copyright (C) 2006
James P. Steiner, www.turtlezero.com, Used with permission."