An Overview of Particle Swarm Optimization Variants

Imran, Muhammad; Hashim, Rathiah; Khalid, Norlin

doi:10.1016/j.proeng.2013.02.063

Cited by 146 publications

(69 citation statements)

References 16 publications

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“…Numerical modeling of RWGs is an important step for the design and can provide quantitative information such as diffraction efficiency and fabrication tolerances, especially for complex or realistic structures where analytical models cannot be directly applied. The optimization of photonic and plasmonic arrays for a specific figure of merit, such as the field enhancement or the diffraction efficiency, can be accelerated using specific optimization algorithms, such as genetic algorithm or particle swarm optimization (PSO) . Nevertheless, the fairly large amount of calculations to find an optimal design requires significant computational time and over the last decades, it has been necessary to develop and improve numerical techniques.…”

Section: Numerical Modelingmentioning

confidence: 99%

“…The optimization of photonic and plasmonic arrays for a specific figure of merit, such as the field enhancement or the diffraction efficiency, can be accelerated using specific optimization algorithms, [65] such as genetic algorithm [66] or particle swarm optimization (PSO). [67] Nevertheless, the fairly large amount of calculations to find an optimal design requires significant computational time and over the last decades, it has been necessary to develop and improve numerical techniques. A variety of methods is available for the numerical simulation of the optical properties of optical micro-and nanostructures, and more specifically RWGs.…”

Section: Numerical Modelingmentioning

confidence: 99%

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Recent Advances in Resonant Waveguide Gratings

Quaranta

Basset

Martin

et al. 2018

Laser & Photonics Reviews

305

199

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Resonant waveguide gratings (RWGs), also known as guided mode resonant (GMR) gratings or waveguide‐mode resonant gratings, are dielectric structures where these resonant diffractive elements benefit from lateral leaky guided modes from UV to microwave frequencies in many different configurations. A broad range of optical effects are obtained using RWGs such as waveguide coupling, filtering, focusing, field enhancement and nonlinear effects, magneto‐optical Kerr effect, or electromagnetically induced transparency. Thanks to their high degree of optical tunability (wavelength, phase, polarization, intensity) and the variety of fabrication processes and materials available, RWGs have been implemented in a broad scope of applications in research and industry: refractive index and fluorescence biosensors, solar cells and photodetectors, signal processing, polarizers and wave plates, spectrometers, active tunable filters, mirrors for lasers and optical security features. The aim of this review is to discuss the latest developments in the field including numerical modeling, manufacturing, the physics, and applications of RWGs. Scientists and engineers interested in using RWGs for their application will also find links to the standard tools and references in modeling and fabrication according to their needs.

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Section: Numerical Modelingmentioning

confidence: 99%

Section: Numerical Modelingmentioning

confidence: 99%

Recent Advances in Resonant Waveguide Gratings

Quaranta

Basset

Martin

et al. 2018

Laser & Photonics Reviews

305

199

View full text Add to dashboard Cite

show abstract

“…The iterative process is repeated until a stopping criterion, such as a predetermined number of generations, is met. There are several variants of the PSO algorithm [16]. For instance, vi in (1) is not affected by Z in the original version of the algorithm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A time performance comparison of particle swarm optimization in mobile devices

et al. 2016

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Abstract. This paper deals with the comparison of three implementations of Particle Swarm Optimization (PSO), which is a powerful algorithm utilized for optimization purposes. Xamarin, a cross-platform development software, was used to build a single C# application capable of being executed on three different mobile operating systems (OS) devices, namely Android, iOS, and Windows Mobile 10, with native level performance. Seven thousand tests comprising PSO evaluations of seven benchmark functions were carried out per mobile OS. A statistical evaluation of time performance of the test set running on three similar devices -each running a different mobile OS-is presented and discussed. Our findings show that PSO running on Windows Mobile 10 and iOS devices have a better performance in computation time than in Android.

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“…PSO has a stable convergence character with great computational efficiency and is easily implemented. A highly capable evolutionary based clustering method by PSO is provided to find the near optimal solution in search space to trounce the previous problems [4].…”

Section: Introductionmentioning

confidence: 99%