Improving the performance of a FBG sensor network using a novel dynamic multi-swarm particle swarm optimizer

A novel wavelength detection technique of overlapping spectra in the serial WDM FBG sensor network

“…From the results, we observe that the average values for both sensors are very close to their real values. The detection accuracies in terms of the wavelength detection errors achieved by Swap-DE are typically smaller than 0.2 pm, which is better than the previous techniques [2][3][4][5][6][7][8].…”

“…Optimization strategy was firstly introduced to determine the Bragging wavelength by Chan et al [2][3][4]. In subsequent works, various evolutionary algorithms (EAs) have been proposed to solve this problem, such as genetic algorithm (GA) [5], dynamic multi-swarm particle swarm optimizer (DMS-PSO) [6,7] and self-adaptive neighborhood search differential evolution plus (SaNSDE+) [8]. These previous investigations demonstrate the potential of the optimization algorithms in detecting the Bragg wavelengths even when the spectra of FBGs are partially or completely overlapped.…”

Multi-objective design of an FBG sensor network using an improved Strength Pareto Evolutionary Algorithm

“…Based on the wavelength detection technique reported in the previous investigations [2][3][4][5][6][7][8], the operational regions of FBGs are permitted to be overlapped partially in our design. This means the bandwidth requirement can be reduced significantly by the overlapping spectra.…”

“…Recently, much attention has been paid to the overlapping spectra-based wavelength detection technique [2][3][4][5][6][7][8]. To improve the multiplexing capability of the WDM system, some evolutionary algorithms, such as genetic algorithm [3], particle swarm optimizer [4,5] and differential evolution algorithm [6,7], are applied to detect the Bragg wavelength when the spectra of FBGs are overlapped. These previous techniques are capable of accurately determining the Bragg wavelengths even when the spectra of FBGs in the network are partially or completely overlapped.…”

A local-best harmony search algorithm with dynamic subpopulations

“…Inspired by the local version of the particle swarm optimization (PSO) algorithm (Blackwell and Branke 2004, Løvbjerg 2001, Liang et al 2007, Liang et al 2006 and the work of Omran and Mahdavi (2008), a new local-best variant of the HS algorithm with dynamic subpopulations, called the DLHS algorithm, is proposed in this article. In the local PSO algorithm, each individual is called a particle with position and velocity.…”