What is Ant Colony Optimization

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In the field of computer sciences and operations research, the ant colony optimization algorithm (ACO) is a probabilistic method for resolving computational issues which can be decreased to resulting best routes via graphs. Artificial Ants stand for multi-agent technique which is inspired from the actual behavior of real ants. Learn more in: Optimizing Learning Weights of Back Propagation Using Flower Pollination Algorithm for Diabetes and Thyroid Data Classification

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Stochastic optimisation procedure imitating the ant foraging behaviour. Method allows to visualize the path through the sequence space. Learn more in: In Machina Systems for the Rational De Novo Peptide Design

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Ant Colony Optimization or ACO is a Swarm Intelligence technique inspired by the ability of real ant colonies to efficiently organize the foraging behavior of the colony using chemical pheromone trails as a means of communication between the ants. Learn more in: Bio-Inspired Grid Resource Management

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ACO studies artificial systems that take inspiration from the behavior of real ant colonies and which are used to solve discrete optimization problems. ACO is a population-based approach to the solution of combinatorial optimization problems. The basic ACO idea is that a large number of simple artificial agents are able to build good solutions to hard combinatorial optimization problems via low-level based communications. Learn more in: Simulation Model of Ant Colony Optimization for the FJSSP

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A probabilistic method that searches for the optimal path that mimics the ants while finding the way from food source to nest. Learn more in: Application and Evaluation of Bee-Based Algorithms in Scheduling: A Case Study on Project Scheduling

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The ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Learn more in: Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

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Constructive meta-heuristic based on ant behavior when searching for the food. Learn more in: Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning

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ACO is another popular bio inspired evolutionary algorithm inspired by the behavior of ants in their search for the shortest paths to food source. Learn more in: Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection

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The Ant Colony Optimization (ACO) is a heuristic algorithm that is inspired from the food foraging behavior of ants. Dorigo introduced the first ACO system in his PhD thesis in 1992. Learn more in: Swarm Intelligence in Text Document Clustering

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A non-traditional optimization technique for solving computational problems that searches for an optimal path in a graph, based on the social behaviour of ants seeking a path between their colony and food source. Learn more in: Ant Colony Algorithm for Single Stage Supply Chain

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It indicates a computing technique used for optimization which is inspired by the movement of ants in search of food in nature. Learn more in: Evolutionary Computing: Principles and Applications to Portfolio Optimization

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A probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. They are inspired by the self-organizing abilities of real ants. Learn more in: Ant Colony Optimization for Use in Content Based Image Retrieval

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Algorithm that imitates the behavior of ants when trying to find the shortest path probabilistically between the colony and a food source. Learn more in: Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer

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Ant colony optimization (ACO) is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. In ACO, a set of software agents called artificial ants search for good solutions to a given optimization problem. Learn more in: Quantum Inspired Swarm Optimization for Multi-Level Image Segmentation Using BDSONN Architecture

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Ant Colony Optimization involves a set of algorithms modelled on the foraging behaviour of a colony of natural ants. Learn more in: Swarm Intelligence Approach for Ad-Hoc Networks

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A swarm intelligence optimization technique inspired by the behavior of ants when searching for efficient routes for food sources. Learn more in: Improvement of the Optimization of an Order Picking Model Associated With the Components of a Classic Volkswagen Beetle Using an Ant Colony Approach