Using the Bees Algorithm to Optimise a Support Vector Machine for Wood Defect Classification

This paper describes a new application of the Bees Algorithm to the optimisation of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. The algorithm, which is a swarm-based algorithm inspired by the food foraging behaviour of honey bees, was also employed to select the components making up the feature vectors to be presented to the SVM. The objective of the work was to find the best combination of SVM parameters and data features to maximise defect classification accuracy. The paper presents the results obtained to demonstrate the strengths of the Bees Algorithm as an optimisation tool.