Real-world optimization problems often require an external “modeling engine” that computes fitnesses or data that are then input to an objective function. These programs often have much longer runtimes than evaluating fitnesses solely with built-in compiler routines. The PBM suite of “real world” antenna benchmarks is an example. Using a stochastic optimizer on real-world problems can be quite challenging because every run returns a different “best” fitness. This issue is addressed by making many runs, often hundreds, possibly thousands, in order to generate meaningful statistics, but doing so may be prohibitive if external modeling is required. And even then the statistical nature of the results may obscure true global extrema. In addition, most of the time real-world problems do not come with a well-defined, clearly appropriate objective function. It falls to the practitioner to define one, which in itself can be a daunting task made even more difficult by using a stochastic optimizer because then every candidate fitness function must be evaluated statistically. This paper examines these and related issues by applying CFO to the PBM suite.
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