MAX-MIN Ant System

By Thomas Stützle, Holger H. Hoos. Future generations computer systems, 2000

2,972 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/c67818b2ce1410ba61f29e1f77412fe23c69f346

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Citations in the corpus

Building comprehensible customer churn prediction models with advanced rule induction techniques (2011, methodology): “The ﬁrst ACO algorithm developed was Ant System (Dorigo, Maniezzo, & Colorni, 1996), where ants iteratively construct solutions and add pheromone to the paths corresponding to these solutions.”
Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling (2005, methodology): “Furthermore, we applied an upper bound max and a lower bound min to the pheromone values as proposed in [32] for MAX–MIN ant systems (MMASs).”
Ant Colony Optimization: Overview and Recent Advances (2018, methodology): “ACO algorithm Main references Year TSP Ant System [55, 63, 65] 1991 yes Elitist AS [55, 63, 65] 1992 yes Ant-Q [74] 1995 yes Ant Colony System [60, 61, 75] 1996 yes MMAS [146, 147, 148] 1996 yes Rank-based AS [29, 30] 1997 yes ANTS [104, 105] 1998 no Best-Worst AS [36, 37] 2000 yes Population-based ”
Swarm Intelligence in Optimization (2008, background): “Stützle and Hoos [164] Quadratic assignment problem Maniezzo [109] Maniezzo and Colorni [111]”
The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances (2003, methodology): “The TSP is the problem of finding a minimal length Hamiltonian circuit of the graph, where an Hamiltonian circuit is a closed tour visiting exactly once each of the n = |N | nodes of G.”
A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP (2007, methodology): “Several ACO algorithms have been proposed which are included within the ACO metaheuristic [11,18,21], such as the Ant System [20], the ant colony system [19], the Max–Min ant system [45], the rank-based ant system [4], and the best–worst ant system [8–10].”
A Comprehensive Review of Swarm Optimization Algorithms (2015, methodology): “The approach was introduced by Stutzle and Hoos in 2000 and it limits the pheromone trail values within the interval of [ τ min , τ max ] [38].”
The hyper-cube framework for ant colony optimization (2004, methodology): “(b) Example for the pheromone update in the HCF. metaheuristic: Ant Colony System (ACS) and Ant System .”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “To alleviate this problem, Balaprakash et al. [63] proposed the iterative application of F -Race, which is abbreviated to iterated F -Race or I / F -Race and showed its effectiveness through examples on MAX–MIN ant system (MMAS) [64], estimation-based local search [65], and SA algorithm.”
Ant colony optimization (2006, positive): “This algorithm [15] is an improvement over the original Ant System.”
Ant colony optimization for resource-constrained project scheduling (2000, background): “A somewhat similar strategy in [36] was to switch between generations where the elitist ant updates pheromone according to the best solution found so far with generations where the update is done according to the best solution in that generation.”