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May 17, 2020 · Ant Colony Optimization technique is purely inspired from the foraging behaviour of ant colonies, first introduced by Marco Dorigo in the 1990s. Ants are eusocial insects that prefer community survival and sustaining rather than as individual species.
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For example: an ant traveled a path: [ (0 -> 3) (distance: 8), (3 -> 5) (distance: 2)] 0.125 units of pheromone would be deposited on pheromone [0,3] += 0.125 and pheromone [3,5] += 0.5. This is done to encourage ants to give more priority to shorter routes between cities.
Jan 8, 2024 · This tutorial introduces the Ant Colony Optimization algorithm. You can learn about genetic algorithms without any previous knowledge of this area, having only basic computer programming skills. The complete source code for the code snippets in this tutorial is available in the GitHub project.
Sep 6, 2022 · One especially important use-case for Ant Colony Optimization (ACO from now on) algorithms is solving the Traveling Salesman Problem (TSP). This problem is defined as follows: Given a complete graph G with weighted edges, find the minimum weight Hamiltonian cycle.
Jan 21, 2024 · The classic example which lecturers or proponents of Ant Colony Optimization (ACO) use is the double bridge experiment [1], which shows that this algorithm can be used to find the shortest path between two points. (Image of ant from DALL·E 3, put together by author using PowerPoint.)
In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph.
