Self-adaptive stepsize search for automatic optimal design
Knowledge-Based Systems
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In this research, Self-Adaptive Stepsize Search (SASS), a novel heuristic optimization algorithm that has only one control parameter, has been applied to a discrete optimization problem, the automated deduction of fourteen Fourier terms in a radio-frequency (RF) waveform to tune a Langmuir probe. Langmuir probes are diagnostic tools used to determine the ion density and the electron energy distribution in plasma processes. RF plasmas are inherently nonlinear, and many harmonics of the driving fundamental can be generated in the plasma. RF components across the ion sheath formed around the probe distort the measurements made. To improve the quality of the measurements, these RF components can be removed by an active-compensation method. In this research, this was achieved by applying an RF signal to the probe tip that matches both the phase and amplitude of the RF signal generated from the plasma. Here, seven harmonics are used to generate the waveform applied to the probe tip. Therefore, fourteen mutually interacting parameters (seven phases and seven amplitudes) had to be tuned on-line. In previous work it was shown that the SOMA algorithm performed best in this application domain and hence the performance of SASS was compared to SOMA. It was demonstrated that SASS outperformed SOMA even without the need of finding a suitable set of control parameters.