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.
Burke, E. and Kendall, G. (2012) Applying Evolutionary Algorithms the No Fit Polygon to the Nesting Problem. The International Conference on Artificial Intelligence, Monte Carlo Resort, Las Vegas, 28 July 2012, 317-324.
Areibi, S. and Vannelli, A. (1994) Advanced Search Techniques for Circuit Partitioning. In: Pardalos, P. and Wolkowicz, H., Eds., Quadratic Assignment and Related Problems, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 16, 77-99.
Hagen, L. and Kahng, A. (1997) Combining Problem Reduction and Adaptive Multistart: A New Technique for Superior Iterative Partitioning. IEEE Transactions on Computer-Aided Design, 16, 709-717. http://dx.doi.org/10.1109/43.644032
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.