Distributed Optimization by Ant Colonies

By A. Colorni, Marco Dorigo, V. Maniezzo, F. Varela, Paul Bourgine. 1992

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Citations in the corpus

Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “Techniques explored in these first multiagent learning contexts were early ant systems and flocking or herding behavior (Manderick and Moyson 1988; Colorni, Dorigo, and Maniezzo 1992; Banerjee 1992), evolutionary computation (Manderick and Spiessens 1989; Steels 1987, 1988; Paredis 1995), social…”
On the Origin of Deep Learning (2017, background): “Some prominent examples in this family are genetic algorithm (Mitchell, 1998), which simulates natural selection and ant colony optimization algorithm (Colorni et al., 1991), which simulates the cooperation of an ant colony to explore surroundings.”
Evolutionary computation and structural design: A survey of the state-of-the-art (2005, background): “Ant colony algorithms inspired by colonies of real ants and initially proposed for solving combinatorial optimization problems [67,153] and subsequently extended to constrained optimization problems [66,154].”
The Ant Lion Optimizer (2015, unknown): “Some of the most popular algorithms in this ﬁeld are: Genetic Algorithms (GA) [14,15], Particle Swarm Optimization (PSO) [16], Ant Colony Optimization (ACO) [17], Differential Evolution (DE) [18], Evolutionary Programming (EP) [19] [20].”
Ant Colony System for a Dynamic Vehicle Routing Problem (2005, methodology): “The first ACO algorithm, Ant System (AS ), has been proposed by Colorni, Dorigo and Maniezzo (see [3] and [5]) and is based on a computational paradigm inspired by the way real ant colonies function.”
Heuristic Optimization Methods in Econometrics (2009, methodology): “This heuristic, first introduced by Colorni et al. (1992a, 1992b) imitates the way ants search for food and find their way back to their nest. First an ant explores its neighborhood randomly. As soon as a source of food is found it starts to transport food to the nest leaving traces of pheromone on ”
A novel chaotic salp swarm algorithm for global optimization and feature selection (2018, unknown): “Some of the most popular meta-heuristic algorithms are Particle Swarm Optimization (PSO) [1], Artificial Bee Colony (ABC) [2] and Ant Colony Optimization [3].”
Ant colony optimization (2006, unknown): “Usually, the controlling mechanism is achieved either by constraining or by randomizing the set of local neighbor solutions to consider in local search [6].”
An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements (2009, unknown): “ACO is a metaheuristic proposed by Dorigo [28]–[32] in the light of the foraging behavior of ants.”
A review of particle swarm optimization. Part I: background and development (2007, unknown): “PSO (Kennedy and Eberhart 1995, Kennedy et al. 2001) shares some of the characteristics of ACS, except they do not disseminate information via pheromone trails.”
Review of job shop scheduling research and its new perspectives under Industry 4.0 (2017, background): “(5) Early studies of ant colony optimization for JSP Ant colony optimization (ACO) which imitates the process of ant colony foraging is another meta-heuristic presented by Colorni et al. (1991), who also is the ﬁrst researcher using this approach to solve the NP-Hard Traveling Salesman Problem.”
Ant colony system: a cooperative learning approach to the traveling salesman problem (1997, background): “The above behavior of real ants has inspired ant system , an algorithm in which a set of artificial ants cooperate to the solution of a problem by exchanging information via pheromone deposited on graph edges.”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, background): “Author/s: Year Algorithm Area of Application Zhao et al. [83] 2005 HPSO Partner Selection for Virtual Enterprise Yang et al. [84] 2006 PSOSA Global Optimization Gao et al. [85] 2006 HPSO Optimizing Radial Basis Function Chu et al. [87] 2006 ASA-PPSO Global Optimization Sadati et al. [88] 2007 PSO-B-”
Ant Colony Optimization: Overview and Recent Advances (2018, unknown): “Other early publications are [34, 35].”