Peak factor optimization of multi-harmonic signals using artificial bee colony algorithm

• Published in: Measurement : journal of the International Measurement Confederation Vol. 150; p. 107040
• Main Authors: Janeiro, Fernando M., Hu, Yingyue, Ramos, Pedro M.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.01.2020; Elsevier Science Ltd
• Subjects: Algorithms; Amplitudes; Artificial bee colony algorithm; Frequency response; Iterative algorithms; Multi-harmonic signal; Optimization; Peak factor; Phase selection; Search algorithms; Signal generation; Signal processing; Studies; Swarm intelligence; Peak factor; Phase selection; Artificial bee colony algorithm; Multi-harmonic signal
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2019.107040

•Multi-harmonic signals are needed in many applications.•Choice of harmonic phases may reduce the amplitude of multi-harmonic signals.•Artificial Bee Colony algorithm is used for peak factor optimization.•The algorithm is applied to signals with different spectral characteristics.•Results are compared with methods available in the literature. Multi-harmonic signals are useful in many applications to estimate the frequency response of a device. To minimize noise effects, signal power should be maximized within the amplitude limitations of the signal generation and acquisition hardware. Signal power depends only on the harmonic amplitudes, while the signal amplitude also depends on the harmonic phases. Careful choice of these phases allows the minimization of the peak factor which corresponds to the minimization of the signal’s amplitude for a given signal power. Multiple methods have been proposed to minimize a signal’s peak factor, including closed form expressions and iterative algorithms. However, the existence of many local extrema suggests the use of global search algorithms. This paper proposes the use of the artificial bee colony algorithm. The solution is further improved by using a Nelder-Mead Simplex algorithm. The artificial bee colony algorithm results are compared with those of previously published methods.