Comparison of a generalized pattern search and a genetic algorithm optimization method
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Building and HVAC system design can signifi- cantly improve if numerical optimization is used. However, if a cost function that is smooth in the de- sign parameter is evaluatedby a buildingenergy sim- ulation program, it usually becomes replaced with a numerical approximation that is discontinuous in the design parameter. Moreover, many building simula- tion programs do not allow obtaining an error bound for the numerical approximations to the cost func- tion. Thus, if a cost function is evaluated by such a program, optimization algorithms that depend on smoothness of the cost function can fail far from a minimum. For such problems it is unclear how the Hooke- Jeeves Generalized Pattern Search optimization al- gorithm and the simple Genetic Algorithm perform. The Hooke-Jeevesalgorithm dependson smoothness of the cost function, whereas the simple Genetic Al- gorithm may not even converge if the cost function is smooth. Therefore, we are interested in how these algorithms perform if used in conjunctionwith a cost function evaluated by a building energy simulation program. In this paper we show what can be expected from the two algorithmsand compare their performancein minimizing the annual primary energy consumption of an office building in three locations. The problem has 13 design parameters and the cost function has large discontinuities. The optimization algorithms reduce the energy consumption by 7% to 32%, depending on the build- ing location. Given the short labor time to set up the optimization problems, such reductions can yield considerable economic gains.