Hybrid evolutionary computational algorithm for dairy cattle feed cost optimization

A Hybrid Evolutionary Computational Algorithm (HECA) is proposed in this paper. It incorporates Genetic algorithm and Simulated Annealing together in Evolutionary Computational part and quantum concepts that are quantum gate, superposition of states in the improvement of initial population. In this paper HECA has been tested on 6 popular benchmark functions and compared with other algorithms reported in literature. HECA has the strong capability to explore the nonlinear search regions and it is a step forward in the area of hybrid stochastic search. HECA is applied on a real world application of cattle feed optimization for local Dairy. The objective of the present algorithm is to find minimum cost diet from the set of available ingredients while improving the quality of feed. The feed mix obtained from the present algorithm is compared with that of a local Dairy. It is found that the results obtained from the present algorithm are favourable and useful for dairy cattle feed planning and cost optimization.