IMPLEMENTATION OF A GENETIC ALGORITHM FOR ENERGY DESIGN OPTIMIZATION OF LIVESTOCK HOUSING USING A DYNAMIC THERMAL SIMULATOR

A Genetic Algorithm (GA) is an optimization process inspired to natural systems ability of surviving in many different environment through the mechanisms of natural selection and genetics. The pairing of GA-based optimization techniques with dynamic thermal models is a common and effective practice to find energy efficient design solutions. In this paper a genetic algorithm with the ability to dialogue with a dynamic thermal model is implemented. The algorithm, coded in Matlab, works with populations of strings. Each string, that represent a complete design solution, is initially randomly generated by the GA and evaluated in terms of energy performances by the dynamic thermal simulator. A new population is then generated using three different GA stochastic operators, reproduction, crossover and mutation, by selecting, mixing and randomly modifying the fittest solutions of the previous generation. Each generation is evaluated by the thermal model and thus the fitness of the strings, that represent the energy efficiency of the design solutions, improves every cycle till eventually converge to the best solution. This whole methodology is well documented and applied in residential buildings design but can be easily extended to livestock housing. In this paper the algorithm is coded to be applied on a simple sheepfold model in order to optimize only passive design solutions.