Research on particle swarm optimization: a review

Song, Ming; Gu, Guochang

doi:10.1109/icmlc.2004.1382171

Cited by 75 publications

(22 citation statements)

References 22 publications

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“…Particle swarm optimization (PSO) applies variations to a population (swarm) of candidate solutions with respect to a given fitness function to approach a desired solution [8]. In this iterative procedure, the specific local solutions are modified 1041-1135/$25.00 © 2008 IEEE at each iteration based on the best global solution, the previous-best local solution, and its last modification (i.e., inertia).…”

Section: Design Methodsmentioning

confidence: 99%

Proposal for Compact Optical Filters Using Large Index Step Binary Supergratings

Afshinmanesh

Marandi

et al. 2008

IEEE Photon. Technol. Lett.

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Compact optical filters are proposed using an aperiodic grating of fixed element size [i.e., a binary supergrating (BSG)] with a large refractive index step. These filters allow for almost arbitrary wavelength filtering, yet they are more compact than previous demonstrations of BSG. The filters are designed using a combination of Boolean particle swarm optimization (B-PSO) and a one-dimensional transfer matrix method. To demonstrate the compact device size, several 50-m-long single-wavelength transmission filters are demonstrated theoretically, each having a different wavelength while using the same structural parameters. A multiwavelength filter is also proposed in an 80-m-long structure to show the versatility of the large refractive index step BSG. A genetic algorithm is substituted for the B-PSO; however, B-PSO shows better performance here. This work may be applied to produce compact optical filters for intrachip optical networks and compact tunable lasers, while using existing single-step photolithography processes.

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Section: Design Methodsmentioning

confidence: 99%

Proposal for Compact Optical Filters Using Large Index Step Binary Supergratings

Afshinmanesh

Marandi

et al. 2008

IEEE Photon. Technol. Lett.

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“…Thus, one may note that the PSO is implemented through an iterative algorithm. Each particle is initially placed at different hyperspaces and, after each iteration (season), they move around, with an update speed factor, towards the solution of the problem [21]. It is important to notice that all of the hyperspaces are mapped on the domain of the problem and, as it has already been stated, the problem itself is to find the minimum point of a user-defined cost function.…”

Section: Psomentioning

confidence: 99%

“…Other point noteworthy is the role of inertia weight w. The greater this coefficient is, the wider the domain is explored. In other words, higher values of w promotes better the searching for global minimum of the cost function [21]. Nonetheless, there are a couple of issues that can arise from choosing huge values of ω. Firstly, as the ω grows the speed of convergence of the particles is slowed and a tread-off between the quality and the speed of the searching is raised.…”

Section: Psomentioning

confidence: 99%

On the effects of hyper-parameters adjustments to the PSO-GMPPT algorithm for a photovoltaic system under partial shading conditions

Leopoldino¹,

Freitas²,

Monteiro³

2020

EAI Endorsed Transactions on Energy Web

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This paper exploits the performance of the particle swarm optimization (PSO) algorithm for a photovoltaic system under partial shading condition (PSC). Essentially our main contribution consists on analyzing the hyper-parameters adjustment of the PSO algorithm to determine the minimum particle numbers, such that the assertiveness to identify the Global Maximum Power Point (GMPP) be higher than 99%. The database was obtained throughout 5760 simulations based on different test cases. From these test cases, the PSC was applied in 2880 simulations. In the previous work, it was shown the best results based on 5 particles. In this update version, it is also shown the best results for 3, 7 and 9 particles, together with a comparison among them. Furthermore, this paper also presents the simulation results to evaluate the performance of the developed algorithm under transient-and steady-state conditions.

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“…Particle swarm optimization (PSO) originates from the natural behavior of a flock of birds, a school of fish, or a swarm of bee. James Kennedy and Russell Eberhart are the PSO founders which use optimized nonlinear functions [30,31] in this technique. By applying bioinspired algorithm, constraint or unconstraint optimization can be solved efficiently and faster.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Harmonic Reduction of a Single-Phase Multilevel Inverter Using Genetic Algorithm and Particle Swarm Optimization

Goh¹,

Ling²,

Goh³

et al. 2018

Compendium of New Techniques in Harmonic Analysis

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Power inverter play an important role in power system especially with its capability on reducing system size and increase efficiently. The recent research trends of power electronic system are focusing on multilevel inverter topics in optimization on voltage output, reducing the total harmonics distortion, modulation technique, and switching configuration. The research emphasizes the optimization with a fundamental switching frequency method that is the optimized harmonic stepped waveform (OHSW) modulation method. The selective harmonic elimination (SHE) calculation has adapted with genetic algorithm (GA) and particle swarm optimization (PSO) in order to speed up the calculation. Both bioinspired algorithms are compared in terms of total harmonic distortion (THD) and selective harmonic elimination for both equal and unequal sources. The overall result showed that both algorithms have high accuracy in solving the nonlinear equation. However, the genetic algorithm showed better output quality in terms of selective harmonic elimination which overall no exceeding 0.4%. Particle swarm optimization shows strength in finding the best total harmonic distortion where in seven-level cascaded H-bridge multilevel inverter (m=0.8) shows 6.8% only as compared to genetic algorithm. Simulation for three-level, five-level, and seven-level for each multilevel inverter at different circumferences had been done in this research. The result draws out a conclusion where the possibility of having a filterless high-efficient inverter can be achieved.

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Research on particle swarm optimization: a review

Cited by 75 publications

References 22 publications

Proposal for Compact Optical Filters Using Large Index Step Binary Supergratings

Proposal for Compact Optical Filters Using Large Index Step Binary Supergratings

On the effects of hyper-parameters adjustments to the PSO-GMPPT algorithm for a photovoltaic system under partial shading conditions

Harmonic Reduction of a Single-Phase Multilevel Inverter Using Genetic Algorithm and Particle Swarm Optimization

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