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Introduction: Animal diet composition is concerned with the distribution of individual ingredients for ensure the greatest piece of the animal in provisions of yield and weight increase. The purpose of diet composition is to supply that put of nutrient ingredient to the animal that finest fulfill its nutrient supplies 1-4. When formulate the diet of an animal, various feed ingredient are combinations so as to supply the essential nourishment to the animal at diverse stage of production. Animal diet formulation model have been developed for profitable purpose as well as for farm animals enlargement, using diverse form of numerical program for several decades 5-9. To attain the object of optimal and objective diet for maximization of milk yield or weight gain of animal, a number of arithmetical Abstract: A nonlinear formulation of optimum livestock diets, though very practical, has seen a limited research since classical techniques like Kuhn Tucker theory used by some researchers has its own limitations due to the rigidity of mathematical characteristics that should hold good to apply this technique. The present study deals with the importance of linear livestock ration formulation for sahiwal cows of second to fifth lactation number to maximize the milk yield. Its solution is found using heuristic approaches viz Controlled Random Search Technique (RST2). We observe that the results obtained are well acceptable with slight deviations, takes less time due to computer usage, gives more flexibility to the decision maker. The techniques do not guarantee optimal solution but at least gives a solution which is best amongst the many solutions generated during the simulation process. The performance of both the techniques indicates that they can be well implemented for nonlinear livestock ration formulation problem.

Introduction: Algebraic procedures such as pivoting are so powerful for manipulating linear equalities and inequalities thatmany nonlinearprogramming algorithms replace the given problem by an approximating linear problem [1][2][3][4] .Separable programming is a prime example, and also one of the most useful, of these procedures. As inseparable programming, these nonlinear algorithms usually solve several linear approximations by letting thesolution of the last approximation suggest a new one [5][6][7][8] . By using different approximation schemes, this strategy can be implemented in several ways. This sectionintroduces three of these methods, all structured to exploit the extraordinary computational capabilities of thesimplex method and the wide-spread availability of its computer implementation.There are two general schemes for approximating nonlinear programs 9-12 .

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