Nature-inspired metaheuristics | Optimization algorithms and methods
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing. As an example, ant colony optimization is a class of optimization algorithms modeled on the actions of an ant colony. Artificial 'ants' (e.g. simulation agents) locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions. One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey bee, another social insect. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search and shares some similarities with estimation of distribution algorithms. (Wikipedia).
Ant Colony Optimization - Part 1: Introduction
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Ant Colony Optimization - Part 4: Ant System (AS)
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Ant Colony Optimization - Part 3.1: Simple Ant Colony Optimization (SACO)
This video is about Ant Colony Optimization - Part 3.1: Simple Ant Colony Optimization (SACO)
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This Is How Ants Find The Shortest Way To Food (Ant Colony Optimization)
Ants are not really smart but they are somehow able to find the shortest way between the ant nest and the food. How they manage to find out the best way in a complex environment? This video explains all these, how ants communicate when they search for food and what kind of interaction th
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Ant Colony Optimization - Part 2: Stigmergy and Artifical Pheromone
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Ant Colony Optimization - Part 3.2: Simple Ant Colony Optimization (SACO) - Example
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Ant Colony Optimization - Part 3.3: Simple Ant Colony Optimization (SACO) - Detailed Exaplination
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Particle Swarm Optimization (PSO) - Part 1: Introduction
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The Traveling Salesman Problem: When Good Enough Beats Perfect
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From playlist Graph Theory
EMPEX LA 2018 - A Swarm of Processes — Simulating Ant Foraging Behavior... by Will Ockelmann -Wagner
A Swarm of Processes — Simulating Ant Foraging Behavior with OTP by Will Ockelmann-Wagner In this talk, we'll see a simulation of a foraging ant colony that can efficiently find and collect food, built using a separate OTP process for each ant. Along the way we'll look at GenServers, Dyna
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Ant Colony Optimization - Part 6: Ant Colony Systsem (ACS)
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Ants Are a True Superorganism - They Literally Share a Stomach
Good telescope that I've used to learn the basics: https://amzn.to/35r1jAk Get a Wonderful Person shirt: https://teespring.com/stores/whatdamath Alternatively, PayPal donations can be sent here: http://paypal.me/whatdamath Hello and welcome! My name is Anton and in this video, we will tal
From playlist Biology
Rethinking Thinking: How Intelligent Are Other Animals?
Intelligence was once thought to be uniquely human. But researchers have discovered astonishing cognitive abilities in many other species—not just our close cousins like chimps, or fellow mammals like dolphins—but also crows, parrots, and even octopuses. If we consider the intelligence of
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Stanford Seminar - Citadel of One: Individuality and the rise of the machines, Suzanne Sadedin
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Ant architecture: The simple rules of ant construction
Fire ants work together to build complex structures out of their own bodies. Research that reveals the simple rules behind this behaviour could be used to inform robotics. Read more at http://www.nature.com/news/1.22290 12th July 2017
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