Ant colony optimization

By Marco Dorigo, Mauro Birattari, Thomas Stützle. IEEE Computational Intelligence Magazine, 2006

839 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/992342f1ec92fbaca4f6d97a36c7b0f31ed44e61

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Citations in the corpus

An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements (2009, positive): “In this paper, we apply the ACS algorithm [31], which is one of the best ACO algorithms so far [32], to tackle the workflow scheduling problem in grid applications.”
Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization (2011, methodology): “Therefore, a dynamic task scheduling algorithm, such as Ant Colony Optimization (ACO)[8, 9], is appropriate for clouds.”
A Comprehensive Review of Swarm Optimization Algorithms (2015, neutral): “Too high exploration rates result in ants getting lost, while too low values result in an inability to acquire the optimal path [23, 24].”
The irace package: Iterated racing for automatic algorithm configuration (2016, neutral): “We are particularly interested in the tuning of metaheuristics, that is, general-purpose optimization algorithms such as evolutionary algorithms (Goldberg 1989), ant colony optimization (Dorigo and Stützle 2004), and other stochastic local search methods (Hoos and Stützle 2005).”
A Field Guide to Genetic Programming (2008, positive): “The probabilities with which rules were applied were adapted by an ant colony optimisation (ACO) algorithm (Dorigo and Stützle, 2004).”
F-Race and Iterated F-Race: An Overview (2010, neutral): “of nearest neighbors used in the solution construction phase; and q0, the probability of selecting the best neighbor deterministically in the pseudo-random proportional action choice rule; for a detailed definition see [26].”
A novel algorithm for global optimization: Rat Swarm Optimizer (2020, methodology): “There are also other popular swarm-intelligence based techniques which are Ant Colony Optimization (Dorigo et al. 2006), Bee Collecting Pollen Algorithm (BCPA) (Lu and Zhou 2008), Wolf pack search algorithm (Yang et al.”
Simulation optimization: a review of algorithms and applications (2014, neutral): “ch as step-size control, and weighting of candidate Amaran,Sahinidis,Shardaand Bury 15 solutions are part of modern implementations [80]. 3.7.2 Ant colony optimization Ant colony optimization methods [52, 51] are heuristic methods that have been used for combinatorial optimization problems. Conceptu”
Chaotic Krill Herd algorithm (2014, positive): “Since then various nature-inspired meta-heuristic approaches have been proposed such as evolutionary strategy (ES) [1,2], particle swarm optimization (PSO) [27,28,40,56], ant colony optimization (ACO) [4], differential evolution (DE) [10,19,38], firefly algorithm (FA) [7], biogeography-based optimiz”
Cloud task scheduling based on ant colony optimization (2013, neutral): “Many researchers used ACO to solve NP-hard problems such as travelling salesman problem, graph colouring problem, vehicle routing problem and scheduling problem [5, 8].”
Load Balancing of Nodes in Cloud Using Ant Colony Optimization (2012, methodology): “The work aimed at reducing the computation time of each job execution in a grid network by considering the resource status and the size of the given job, the algorithm chooses optimal resources to process the submitted jobs by applying the local and global pheromone update technique to balance the…”