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Ant system: optimization by a colony of cooperating agents
It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS. Expand
Ant colony system: a cooperative learning approach to the traveling salesman problem
The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs. Expand
Swarm intelligence
Optimization, Learning and Natural Algorithms
Swarm Intelligence - From Natural to Artificial Systems
This chapter discusses Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, and its application to Data Analysis and Graph Partitioning. Expand
Distributed Optimization by Ant Colonies
A distributed problem solving environment is introduced and its use to search for a solution to the travelling salesman problem is proposed. Expand
Ant Algorithms for Discrete Optimization
An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented. Expand
AntNet: Distributed Stigmergetic Control for Communications Networks
AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems, and showed superior performance under all the experimental conditions with respect to its competitors. Expand
Ant colony optimization for continuous domains
This paper shows how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure, and compares the results with those reported in the literature for other continuous optimization methods. Expand