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Stützle, T. and Dorigo, M. (2009) ACO Algorithms for the Traveling Salesman Problem. In: Miettinen, K., Makela, M., Neittaanmaki, P. and Periaux, J., Eds., Evolutionary Algorithms in Engineering and Computer Science, Wiley, Hoboken, 1-10.

has been cited by the following article:

• TITLE: No Fit Polygon for Nesting Problem Solving with Hybridizing Ant Algorithms

  AUTHORS: Qiang Yang

  KEYWORDS: Genetic Algorithm, Search, Ant Algorithms, No Fit Polygon, Simulated Annealing

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.7 No.5, May 20, 2014

  ABSTRACT: In design science, these two kinds of problems are mutually nested, however, the nesting could not blind us for the fact that their problem-solving and solution justification methods are different. The ant algorithms research field, builds on the idea that the study of the behavior of ant colonies or other social insects is interesting, because it provides models of distributed organization which could be utilized as a source of inspiration for the design of optimization and distributed control algorithms. In this paper, a relatively new type of hybridizing ant search algorithm is developed, and the results are compared against other algorithms. The intelligence of this heuristic approach is not portrayed by individual ants, but rather is expressed by the colony as a whole inspired by labor division and brood sorting. This solution obtained by this method will be evaluated against the one obtained by other traditional heuristics.