Particle Swarm Optimization and Firefly Algorithm: Performance analysis

Bhushan, Bharat; Pillai, Sarath S.

doi:10.1109/iadcc.2013.6514320

Cited by 37 publications

(17 citation statements)

References 26 publications

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“…Fireflies flashes their light through a process called bioluminescence. [20] The reason of different patterns of their light is used by them is not known truly but it is assumed that it is either used to attract a potential prey or to mate with their counter parts. It can also be used to warn the predators or the enemies about the bitter taste of the fireflies [21].…”

Section: Firefly Algorithmmentioning

confidence: 99%

Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem

Jain¹,

Jain²,

Jain³

et al. 2018

IJSRCSE

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Abstract-Many advancements have been made in the development of optimization algorithms which are based on the evolutionary concept of behavior of biotic creatures like fish, ants, birds etc. This paper compares three such nature inspired algorithms; firefly, particle swarm optimization and ant colony optimization algorithm for optimizing Quadratic Assignment Problem. These algorithms are compared on the grounds of their time of computation of result, the no. of iterations required to solve the problem, and the accuracy .

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Section: Firefly Algorithmmentioning

confidence: 99%

Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem

Jain¹,

Jain²,

Jain³

et al. 2018

IJSRCSE

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“…The procedure for designing flower shaped microstrip patch antenna using ANN and FA algorithms is discussed in [32]. The use of two different algorithms such as PSO and FA on ten nonlinear benchmark functions is explained in [33]. Kaur and Sivia [34] have illustrated hybrid fractal antenna geometry for biomedical applications by optimizing its feed position through ANN and PSO.…”

Section: Introductionmentioning

confidence: 99%

Ann and Fa Based Design of Hybrid Fractal Antenna for Ism Band Applications

Kaur¹,

Sivia²

2020

PIER C

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In this paper, a compact Giuseppe Peano, Cantor Set, and Sierpinski Carpet fractals based hybrid fractal antenna (GCSA) is designed and developed for Industrial, Scientific and Medical (ISM) band applications. The proposed GCSA is a hybrid fractal design which is created by fusing Giuseppe Peano, Cantor set, and Sierpinski carpet fractals together. The optimization of the microstrip line feed position is performed by using a Firefly Algorithm (FA). The substrate material employed for proposed GCSA is a low-priced, easily available FR4 epoxy of thickness 1.6 mm. By varying the geometrical dimensions of the radiating patch, a data set of 58 GCSAs is randomly generated for the realization of Artificial Neural Network (ANN) and FA approaches. The designed structure is fabricated, and then measured results are evaluated. The proposed GCSA is capable of resonating at 2.4450 GHz with S(1, 1) < −10 dB. The measured bandwidth of the operating ISM band is 101 MHz. The quantitative performance of three different ANN types reveals that Feed Forward Back Propagation ANN (FFBPN) shows minimum error in comparison to other two ANN types. The simulated, experimental, and optimized results show a good match that specifies the preciseness of the measurement.

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“…The PSO algorithm is based on the behavior of the bees when they look for areas with a significant amount of pollen (Bhushan et al, 2013) (Hu, et al, 2015). To this aim, each particle (bee) carries out a search and memorizes the best location found, i.e.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The algorithm to set up the initial paths is based on the work presented in (Cui et al, 2003) and (Mu et al, 2009). In this paper the evaluation of five algorithms based on multiobjective computational intelligence used to solve the RWA problem is presented, namely: the Firefly Algorithm (FA) (Rubio-Largo et al, 2012), the Differential Evolutionary Algorithm (DEA) (Quintero et al, 2004), the Simulated Annealing Algorithm (SAA) (Hernández et al, 2011) and two modifications of the Particle Swarm Optimization (PSO) algorithm (Bhushan et al, 2013). The evaluation is accomplished by varying the number of available wavelengths in the network and the capacity of the nodes to perform wavelength conversion.…”

Section: Introductionmentioning

confidence: 99%

Performance of multiobjective computational intelligence algorithms for the routing and wavelength assignment problem

2016

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This paper presents an evaluation performance of computational intelligence algorithms based on the multiobjective theory for the solution of the Routing and Wavelength Assignment problem (RWA) in optical networks. The study evaluates the Firefly Algorithm, the Differential Evolutionary Algorithm, the Simulated Annealing Algorithm and two versions of the Particle Swarm Optimization algorithm. The paper provides a description of the multiobjective algorithms; then, an evaluation based on the performance provided by the multiobjective algorithms versus mono-objective approaches when dealing with different traffic loads, different number of wavelengths and wavelength conversion process over the NSFNet topology is presented. Simulation results show that monoobjective algorithms properly solve the RWA problem for low values of data traffic and low number of wavelengths. However, the multiobjective approaches adapt better to online traffic when the number of wavelengths available in the network increases as well as when wavelength conversion is implemented in the nodes.

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Particle Swarm Optimization and Firefly Algorithm: Performance analysis

Cited by 37 publications

References 26 publications

Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem

Comparative Study between FA, ACO, and PSO Algorithms for Optimizing Quadratic Assignment Problem

Ann and Fa Based Design of Hybrid Fractal Antenna for Ism Band Applications

Performance of multiobjective computational intelligence algorithms for the routing and wavelength assignment problem

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