Abstract
Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.
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