Proceedings ArticleDOI
MAX-MIN Ant System and local search for the traveling salesman problem
Thomas Stützle,Holger H. Hoos +1 more
- pp 309-314
TLDR
The results clearly show that MAX-MIN Ant System has the property of effectively guiding the local search heuristics towards promising regions of the search space by generating good initial tours.Abstract:
Ant System is a general purpose algorithm inspired by the study of the behavior of ant colonies. It is based on a cooperative search paradigm that is applicable to the solution of combinatorial optimization problems. We introduce MAX-MIN Ant System, an improved version of basic Ant System, and report our results for its application to symmetric and asymmetric instances of the well known traveling salesman problem. We show how MAX-MIN Ant System can be significantly improved, extending it with local search heuristics. Our results clearly show that MAX-MIN Ant System has the property of effectively guiding the local search heuristics towards promising regions of the search space by generating good initial tours.read more
Citations
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Journal ArticleDOI
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: 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.
Book
Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
Journal ArticleDOI
Ant algorithms for discrete optimization
TL;DR: 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.
Journal ArticleDOI
MAX-MIN Ant system
Thomas Stützle,Holger H. Hoos +1 more
TL;DR: Computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MM AS is currently among the best performing algorithms for these problems.
Book ChapterDOI
Ant Colony Optimization
TL;DR: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species as discussed by the authors.
References
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Journal ArticleDOI
Ant system: optimization by a colony of cooperating agents
TL;DR: 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.
Proceedings Article
Distributed Optimization by Ant Colonies
TL;DR: A distributed problem solving environment is introduced and its use to search for a solution to the travelling salesman problem is proposed.
Journal ArticleDOI
TSPLIB—A Traveling Salesman Problem Library
TL;DR: This paper contains the description of a traveling salesman problem library (TSPLIB) which is meant to provide researchers with a broad set of test problems from various sources and with various properties.
Journal ArticleDOI
Greedy Randomized Adaptive Search Procedures
TL;DR: This paper defines the various components comprising a GRASP and demonstrates, step by step, how to develop such heuristics for combinatorial optimization problems.