Ant colony optimization: Introduction and recent trends

By Christian Blum. 2005

1,342 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c692e5f5b943963fe3e92938e156d9daab6057c9

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Citations in the corpus

A comprehensive survey: artificial bee colony (ABC) algorithm and applications (2012, methodology): “2001; Reyes-Sierra and Coello 2006; Blum 2005; Dorigo and Blum 2005). The self-organization and division of labour features (Bonabeau et al. 1999) and the satisfaction principles (Millonas 1994) required by SI are strongly and clearly can be seen in honey bee colonies, the researchers have recently ”
A survey on metaheuristics for stochastic combinatorial optimization (2009, methodology): “Among recent overviews, we cite the one by Blum (2005), which also highlights new research trends.”
A survey: algorithms simulating bee swarm intelligence (2009, background): “So many papers related with their applications have been presented to the literature and several survey papers regarding these studies can be found in the literature (Eberhart et al. 2001; Sierra and Coello 2006; Blum 2005; Dorigo and Blum 2005).”
A survey on optimization metaheuristics (2013, background): “A survey on theoretical results on ACO and its most notable applications are discussed in [74,31,71,70,8].”
An advanced ACO algorithm for feature subset selection (2015, neutral): “[3].”
Fundamentals of natural computing: an overview (2007, methodology): “Theoretical investigations into ACO include convergence analysis [128]; the comparison with other approaches, like optimal control [31] and gradientbased search [193]; and the hybridization with more classical artificial intelligence and operations research methods [34].”
Hybrid metaheuristics in combinatorial optimization: A survey (2011, methodology): “ACO is a population-based metaheuristic inspired by the ant foraging behavior and further formalized as a model-based search metaheuristic [67, 68].”
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems (2011, neutral): “All rights reserved. doi:10.1016/j.cad.2010.12.015 the source [8,9], HS works on the principle of music improvisation in music players [10] and GEM works on the principle of the explosion of a grenade [11].”