Ant colony system: a cooperative learning approach to the traveling salesman problem

By Marco Dorigo, Luca Maria Gambardella. 1997

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Citations in the corpus

Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “Two paradigms that we consider as particularly appropriate in this respect are transfer learning (for example, Taylor and Stone [2009, 2011]) and swarm intelligence (Dorigo and Gambardella 1997, Dorigo and Stützle 2004).”
Practical Genetic Algorithms (2005, background): “, 1983), particle swarm optimization (Parsopoulos and Vrahatis, 2002), ant colony optimization (Dorigo and Maria, 1997), and evolutionary algorithms (Schwefel, 1995).”
An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing (2018, methodology): “With major improvements over the original ACO algorithms, the ACS was proposed by Dorigo and Gambardella [43] initially for solving the traveling salesman problem (TSP).”
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications (2009, background): “Recently natural swarm inspired algorithms like Particle Swarm Optimization (PSO) [5], Ant Colony Optimization (ACO) [6] have found their way into this domain and proved their effectiveness.”
Ant colony optimization: Introduction and recent trends (2005, methodology): “Rank-based AS (RAS) Bullnheimer, Hartl, and Strauss [21] MAX–MIN Ant System (MMAS) Stützle and Hoos [91] Ant Colony System (ACS) Dorigo and Gambardella [33] Hyper-Cube Framework (HCF) Blum and Dorigo [11]”
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “Refinements of the original ACO algorithm improve exploitation of good solutions [25,45,36], e.”
Application of decision-making techniques in supplier selection: A systematic review of literature (2013, methodology): “ACA is a typical AI optimization method (Dorigo & Gambardella, 1997; Dorigo, Maniezzo, & Colorni, 1996) that simulates a colony of artificial ants that aid one another to obtain effective solutions in complex optimization problems.”
Ant colony optimization theory: A survey (2005, background): “Examples are ant colony system (ACS) [21] and MAX – MIN Ant System ( MM AS) [66], which are among the most successful ACO variants in practice.”
Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization (2011, methodology): “Many researchers used ACO to solve NP-hard problems such as traveling salesman problem[10], graph coloring problem[11], vehicle routing problem[12], and so on.”
Ant Colony Optimization: Overview and Recent Advances (2018, methodology): “ACS [60, 61, 75] improves over AS by increasing the importance of exploitation of information collected by previous ants with respect to exploration of the search space.”
Classification With Ant Colony Optimization (2007, methodology): “Note that other variants of the initial Ant System have been proposed as well, such as Ant Colony System [15], rank-based Ant System [16], and Elitist Ant System [12].”
Flowshop-scheduling problems with makespan criterion: a review (2005, methodology): “ACO has been applied to solve diﬀerent types of combinatorial optimization problems including the TSP (Dorigo and Gambardella 1997), QAP (Quadratic Assignment Problem) (Gambardella et al .”
AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling (2017, methodology): “A scheduling algorithm based on Ant Colony Optimization (ACO) [9] is designed to solve the assignment”