Ant Colony Optimization: A need of an Hour

 

1. Background:

   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. (Details on this behaviour.)

   Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over faster, and thus the pheromone density remains high as it is laid on the path as fast as it can evaporate.

   Thus, when one ant finds a good (short, in other words) 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. 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. They have an advantage over simulated annealing and genetic algorithm approaches when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in network routing.

2. Particle swarm optimization

   Particle swarm optimization (PSO) is form of swarm intelligence. Imagine a swarm of insects or a school of fish. If one sees a desirable path to go (ie for food, protection, etc.) the rest of the swarm will be able to follow quickly even if they are on the opposite side of the swarm.

   This is modeled by particles in multidimensional space that have a position and a velocity. These particles are flying through hyperspace and remember the best position that they have seen. Members of a swarm communicate good positions to each other and adjust their own position and velocity based on these good positions.

3. Swarm intelligence

   Swarm intelligence (SI) is an artificial intelligence technique based around the study of collective behaviour in decentralised, self-organised, systems. The expression "swarm intelligence" was introduced by Beni & Wang in 1989, in the context of cellular robotic systems (see also cellular automata).

   SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralised control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behaviour. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling.

   Two of the most successful swarm intelligence techniques currently in existence are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). ACO is a metaheuristic that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants, deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems. PSO is a global minimisation technique for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimisation strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.

4. Applications of Swarm Technology

Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. NASA is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors. Swarm technology is particularly attractive because it is cheap, robust, and simple.

Further Studies can be done from these external links:

• http://www.aco-metaheuristic.org/
• www.swarm-bots.org
• www.kewlschool.com
• www.particleschool.info
• OnLine Encylopedias are also free sources.