Ant Colony Optimization

Suppose to connect the nest of a colony of Argentine ants to a food source by two bridges of equal lengths. Each ant starts exploring the neighborhood of the nest, randomly selects one of the two bridges and eventually reaches the food source. Along the path between food and nest, each ant deposits pheromone on the ground to mark the path that could lead other ants to food. Due to randomness, after some time one of the two bridges has a higher amount of pheromone attracting more ants. The more a...
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Particle Swarm optimization

A swarm of bees flies across a field to find the points with the highest density of flowers. Initially, when no knowledge on the field has been gained yet, the bees move randomly. Each bee remembers the locations where it found the most flowers and knows from the others the other locations with high concentration of flowers. After the first random exploration, each bee is simultaneously pulled to returning to the location where it had found the most flowers and to the location reported by the f...
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Genetic Algorithms

  Genetic algorithms mimic the process of natural selection to solve optimization and search problems appearing in a huge number of very different application fields, from bioinformatics to economics and engineering. Suppose for example that you need to design a certain electronic equipment according to some specifications. This is an "optimization" problem in that the "optimal" solution will be one of the possibly many equipments meeting the specifications. Genetic algorithms serve i...
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