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Ant system: optimization by a colony of cooperating agents
- M. Dorigo, V. Maniezzo, A. Colorni
- Computer ScienceIEEE Trans. Syst. Man Cybern. Part B
- 1 February 1996
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.
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.
An iterative particle swarm embedding algorithm (PSEA) that learns embeddings of low-dimensional representations for high-dimensional input patterns and achieves competitive or even better embedding like the related methods locally linear embedding, and ISOMAP.
Swarm Intelligence - From Natural to Artificial Systems
- E. Bonabeau, M. Dorigo, G. Theraulaz
- Computer ScienceSanta Fe Institute Studies in the Sciences of…
This chapter discusses Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, and its application to Data Analysis and Graph Partitioning.
Ant Colony Optimization Theory
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.
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.
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.