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Application of ant colony optimization to inspection planning

: Schmitt, R.; Zheng, H.; Zhao, X.; König, N.; Coelho, R.R.


IEEE Instrumentation and Measurement Society; IEEE Computational Intelligence Society:
Computational Intelligence for Measurement Systems and Applications, 2009. CIMSA '09 : May 11-13, 2009, Hong Kong
Piscataway: IEEE, 2009
ISBN: 978-1-4244-3819-8
International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) <2009, Hong Kong>
Conference Paper
Fraunhofer IPT ()

Within this paper the application of an ant colony optimization (ACO) algorithm to inspection planning is presented. Since inspection planning is a time consuming task, optimizing these activities plays a major role in the quality inspection field. In this paper the extraction procedures of local inspection path planning (LIPP) and measurement device selection (MDS) to travelling salesman problem (TSP) and subset problem are presented respectively. An ACO algorithm based on Max-Min Ant System (MMAS) is presented for solving the problems. Experiment on industrial workpiece shows the applicability of ACO to inspection planning.