Dynamic multi-swarm particle swarm optimizer

Liang, Jing; Suganthan, Ponnuthurai Nagaratnam

doi:10.1109/sis.2005.1501611

Cited by 321 publications

(254 citation statements)

References 13 publications

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“…In each iteration of the algorithm, a new location (combination of CF parameters) for the particle is calculated based on its previous location and velocity vector (velocity vector contains particle velocity for each dimension of the problem). Within this research the PSO algorithm with global topology (GPSO) [6] was utilized. The chaotic PRNG is used in the main GPSO formula (1), which determines a new "velocity", thus directly affects the position of each particle in the next iteration.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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Multiswarm PSO with supersized swarms - Initial performance study

Pluháček

Šenkeřík

Zelinka

2016

AIP Conference Proceedings

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…The previous research presented in [5] led to the idea of implementing this principle into multi-swarm PSO. The multi-swarm approach for PSO [6] is very popular in recent years.…”

Section: Introductionmentioning

confidence: 99%

Multiswarm PSO with supersized swarms - Initial performance study

Pluháček

Šenkeřík

Zelinka

2016

AIP Conference Proceedings

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“…For optimum system performance, high radiation efficiency, small volume, simple and low-loss impedance matching to receive and transmit paths is prerequisites of the antennas [3]. Micro strip antennas have attracted much interest due to their small size, lightweight, low cost on mass production, low profile and easy integration with other components [7], [13]. The micro strip antenna is less suitable for modern communication system.…”

Section: Scope and Structure Of Micro Strip Patch Antennamentioning

confidence: 99%

Optimization of MSA with Swift Particle Swarm Optimization

Rathi¹,

Rathi²,

Vijay³

2010

IJCA

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In this Paper a new designing method is proposed for Circular Patch Micro strip Antenna (MSA) using an artificial search Algorithm named Particle swarm Optimization (PSO). It needs two stages for designing. Firstly circular patch MSA antenna needs modelling using some benchmark function. Then in second stage it's require inverse modelling using an artificial search algorithm (PSO) with some constraints. According to above steps first bandwidth of MSA is modelled using bench mark function as an input and resulted output are in form of frequency range, circular patch radius (r), ground plane length, substrate thickness, electrical thickness and dielectric loss tangent using Artificial search method. This paper presents the strategy that at the starting process cognition-learning random factor has more effect then social learning random factor. Gradually social learning random factor has more impact after learning cognition random factor to find out global best. The aim is to find out under above circumstances these modifications in PSO (Swift PSO) can give better result for optimization of Micro Strip Antenna (MSA).

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“…Most of the articles [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] provide enough evidence of the fact that Swarm based optimization is far more effective that stochastic optimization method for antenna array design problems. In general, the superiority of such swarm based method for solving any kind of engineering optimization problems are proved in terms of benchmark problems in different articles [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%