The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Applying Ant Algorithms and the No Fit Polygon to the Nesting Problem
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Applying Ant Algorithms and the No Fit Polygon to the Nesting Problem
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
A parallel algorithm for the two-dimensional cutting stock problem
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
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In previous work solutions for the nesting problem are produced using the no fit polygon (NFP), a new evaluation method and three evolutionary algorithms (simulated annealing (SA), tabu search (TS) and genetic algorithms (GA)). Tabu search has been shown to produce the best quality solutions for two problems. In this paper this work is developed. A relatively new type of search algorithm (ant algorithm) is developed and the results from this algorithm are compared against SA, TS and GA We discuss the ideas behind ant algorithms and describe how they have been implemented with regards to the nesting problem. The evaluation method used is described, as is the NFP. Computational results are given.