Parallel ant colonies for combinatorial optimization problems
Ant Colonies (AC) optimization take inspiration from the behavior of real ant colonies to solve optimization problems. This paper presents a parallel model for ant colonies to solve the quadratic assignment problem (QAP). Parallelism demonstrates that cooperation between communicating agents improve the obtained results in solving the QAP. It demonstrates also that high-performance computing is feasible to solve large optimization problems.
KeywordsLocal Search Tabu Search Combinatorial Optimization Problem Tabu List Quadratic Assignment Problem
Unable to display preview. Download preview PDF.
- 1.M. Dorigo. Optimization, learning and natural algorithms. PhD thesis, Politecnico di Milano, Italy, 1992.Google Scholar
- 3.L. Gambardella, E. Taillard, and M. Dorigo. Ant colonies for the qap. Technical Report 97-4, IDSIA, Lugano, Switzerland, 1997.Google Scholar
- 4.B. Bullnheimer, R. F. Hartl and C. Strauss. Applying the ant system to the vehicle routing problem. In 2nd Metaheuristics Int. Conf. MIC’97, Sophia-Antipolis, France, July 1997.Google Scholar
- 6.R. Schoonderwoerd, O. Holland, J. Bruten, and L. Rothkrantz. Ant-based load balancing in telecommunications networks. Adaptive behavior, 5(2):169–207, 1997.Google Scholar
- 9.E. G. Talbi, Z. Hafidi, and J.-M. Geib. Parallel adaptive tabu search for large optimization problems. In 2nd Metaheuristics International Conference MIC’97, pages 137–142, Sophia-Antipolis, France, July 1997.Google Scholar
- 10.E. D. Taillard and L. Gambardella, Adaptive memories for the quadratic assignment problem. Technical Report 87-97, IDSIA, Lugano, Switzerland, 1997.Google Scholar
- 12.P. Hansen and N. Mladenovic, An introduction to variable neighborhood search. In 2nd Metaheuristic Int. Conf., Sophia-Antipolis, France, 1997.Google Scholar
- 13.E-G. Talbi, J-M. Geib, Z. Hafidi, and D. Kebbal. A fault-tolerant parallel heuristic for assignment problems. In BioSP3 Workshop on Biologically Inspired Solutions to Parallel Processing Systems, in IEEE IPPS/SPDP’98 (Int. Parallel Processing Symposium /Symposium on Parallel and Distributed Processing.Google Scholar