{"title":"Optimization Approaches for a Complex Dairy Farm Simulation Model","authors":"Jagannath Aryal, Don Kulasiri, Dishi Liu","volume":14,"journal":"International Journal of Computer and Information Engineering","pagesStart":405,"pagesEnd":412,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/935","abstract":"This paper describes the optimization of a complex\r\ndairy farm simulation model using two quite different methods of\r\noptimization, the Genetic algorithm (GA) and the Lipschitz\r\nBranch-and-Bound (LBB) algorithm. These techniques have been\r\nused to improve an agricultural system model developed by Dexcel\r\nLimited, New Zealand, which describes a detailed representation of\r\npastoral dairying scenarios and contains an 8-dimensional parameter\r\nspace. The model incorporates the sub-models of pasture growth and\r\nanimal metabolism, which are themselves complex in many cases.\r\nEach evaluation of the objective function, a composite 'Farm\r\nPerformance Index (FPI)', requires simulation of at least a one-year\r\nperiod of farm operation with a daily time-step, and is therefore\r\ncomputationally expensive. The problem of visualization of the\r\nobjective function (response surface) in high-dimensional spaces is\r\nalso considered in the context of the farm optimization problem.\r\nAdaptations of the sammon mapping and parallel coordinates\r\nvisualization are described which help visualize some important\r\nproperties of the model-s output topography. From this study, it is\r\nfound that GA requires fewer function evaluations in optimization\r\nthan the LBB algorithm.","references":"[1] R.P.S. Hart, M.T. Larcombe, R.A. Sherlock, and L.A. Smith,\r\nOptimization techniques for a computer simulation of a pastoral dairy\r\nfarm, Computers and Electronics in Agriculture, 19 (1998), 129-153.\r\n[2] M.C. Fu , Optimization via simulation: a review, Annals of Operations\r\nResearch, 53 (1994), 199-247.\r\n[3] D.G.Mayer, B. P. Kinghorn, and A.A. Archer, Differential evolution - an\r\neasy and efficient evolutionary algorithm for model optimisation,\r\nAgricultural Systems, 83 (2005), 315-328.\r\n[4] A.E. Dooley, W.J. Parker, and H.T. Blair, Modelling of transport costs\r\nand logistics for on-farm milk segregation in New Zealand dairying,\r\nComputers and Electronics in Agriculture, 48 (2005), 75-91.\r\n[5] The Treasury. (2007, November 25). Available:\r\nhttp:\/\/www.treasury.govt.nz\/economy\/overview\/2007\/07.htm#_tocPrim\r\nary_Industries\r\n[6] R.A. Sherlock, K.P. Bright and P.G. Neil, An Object-Oriented Simulation\r\nModel of a Complete Pastoral Dairy Farm, Proceeding of the\r\ninternational conference on Modelling and Simulation Modelling and\r\nSimulation Society of Australia (1997),Hobart.\r\n[7] E. Post, Adventures with portability. Third LCI International Conference\r\non Linux Clusters: The HPC Revolution 2002, Florida (2002), USA.\r\n[8] D.G. Mayer, J.A. Belward, and K. Burrage , Robust parameter settings of\r\nevolutionary algorithms for the optimisation of agricultural systems\r\nmodels, Agricultural Systems, 69 (2001), 199-213\r\n[9] J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor,\r\nMichigan: The University of Michigan Press, 1975.\r\n[10] S.J. Mardle, S. Pascoea and M. Tamizb, An investigation of genetic\r\nalgorithms for the optimization of multi-objective fisheries bioeconomic\r\nmodels, International Transaction in Operational Research,7.(2000), 33 -\r\n49\r\n[11] M.J. Chen, K.N. Chen, and C.W. Lin, Optimization on response surface\r\nmodels for the optimal manufacturing conditions of dairy tofu, Journal of\r\nFood Engineering, 68 (2005), 471- 480.\r\n[12] L.G. Barioni, C.K.G. Dake, and W.J. Parker, Optimizing rotational\r\ngrazing in sheep management systems, Environment International, 25,\r\n617 (1999), 819-825.\r\n[13] R.G. Strongin, On the convergence of an algorithm for finding a global\r\nextremum, Engineering Cybernetics 11 (1973), 549-555.\r\n[14] Y.D. Sergeyev, D. Famularo, and P. Pugliese, Index branch-and-bound\r\nalgorithm for Lipschitz univariate global optimization with multiextremal\r\nconstraints, Journal of Global Optimization, 21, (2001) 317-341.\r\n[15] R. Horst, P.M. Pardalos, and Thoai N.V., Introduction to Global\r\nOptimization, Kluwer Academic Publishers, Amsterdam, 1995\r\n[16] E. Gourdin, P. Hansen and B. Jaumard, Global optimization of\r\nMultivariate Lipschitz Functions: Survey and Computational\r\nComparison, Les cahier du GERAD, 1994.\r\n[17] P. Hansen and B. Jaumard, Lipschitz optimization In R. Horst and P. M.\r\nPardalos (eds.) Handbook of Global Optimization (pp 407- 487).\r\nDordrecht\/Boston\/London: Kluwer Academic Publishers, 1995\r\n[18] L. Padula and M.A. Arbib, System Theory, W.B. Saunders, Philadelphia,\r\n1974.\r\n[19] S. J. R. Woodward, Dynamical systems models and their application to\r\noptimising grazing management, In R.M. Peart and R.B. Curry (eds.),\r\nAgricultural Systems Modelling and Simulation, Marcel Dekker, 1997.\r\n[20] W. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Parallel\r\nProgramming with the Message Passing Interface, The MIT Press,\r\nMassachusetts, 1994.\r\n[21] J.W. Sammon, A non-linear mapping for data analysis. IEEE\r\nTransactions on computers, 5 (1969), 401-409.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 14, 2008"}