OPTIMIZATION TECHNIQUES FOR A COMPUTER-SIMULATION OF A PASTORAL DAIRYFARM

This paper compares different methods of optimising the management var iables in UDDER, a commercially-available computer simulation model of a pastoral dairy farm. The emphasis is on identifying the best optimi sation strategy for this complex multi-dimensional system, taking the simulation model as a given constant. The optimisation methods studied are based on significantly different principles, with differing stren gths and weaknesses: two hill-climbing algorithms (Nelder-Mead simplex and Powell's direction set), and a genetic algorithm (GA). Rather tha n examine all facets of dairy farm management, a single problem is opt imised-that of maximising milkfat production while maintaining the hea lth of the herd and pasture. The results show that while the GA can de termine good regions within the search space quickly, it is considerab ly slower than either hill-climber at finding the optimal point within that region. The hillclimbers, in contrast, are fast but have a tende ncy to get trapped on local maxima and thus fail to find the true opti mum. This led to the development of a hybrid algorithm which utilises the initial global search of the GA, followed by the more efficient lo cal search of a hill-climber. This hybrid algorithm discovered near-op timal points much more quickly than the GA, and with more reliability than the hill-climber. (C) 1998 Elsevier Science B.V.