Combustion optimization of a boiler based on the chaos and Lévy flight vortex search algorithm

Li, Xia; Niu, Peifeng; Jian-ping, Liu

doi:10.1016/j.apm.2018.01.043

Cited by 104 publications

(36 citation statements)

References 23 publications

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“…• individual coprocessor cores for P ∈ [12,180], • 1-2 logical cores for P ∈ (180, 360], and • 3-4 logical cores for P ∈ (360, 720]. In Fig.3(a) a nonlinear scale was used, where for P ∈ [12,360] a measurement with the step size of 12 • P was implemented, whereas for P ∈ (360, 720] the size of 24 • P was used. To illustrate the difference in t par (P, N ) computing time a nonlinear gray scale was applied in Fig.3(b).…”

Section: Parallel Computation Of Bifurcation Diagrams With Two Vamentioning

confidence: 99%

Using the 0-1 Test for Chaos in Nonlinear Continuous Systems With Two Varying Parameters: Parallel Computations

2019

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Two-parameter diagrams obtained through the 0-1 test of chaos for nonlinear oscillatory continuous systems are presented in this paper. The diagrams are the results of a parallel approach to tackle enormous memory and computational time requirements due to the known oversampling problem associated with the use of the 0-1 test for chaos in continuous systems. Our rectangular diagrams with black-and-white shades of gray levels correspond to the numbers between 0 and 1 obtained as the result of the 0-1 test for chaos. A comparison between the two-parameter diagrams for the 0-1 test with the color bifurcation diagrams for oscillatory systems obtained from another method (period-n identification) is also considered. Illustrative examples are based on both the well-known Lorenz model and a model describing two equivalent electric arc circuits. INDEX TERMS The 0-1 test of chaos, two-parameter bifurcation diagrams, oscillatory continuous dynamics, parallel computation, electric arc circuits.

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Section: Parallel Computation Of Bifurcation Diagrams With Two Vamentioning

confidence: 99%

Using the 0-1 Test for Chaos in Nonlinear Continuous Systems With Two Varying Parameters: Parallel Computations

2019

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“…LV mechanism uses the random walk to develop the search capability. This mechanism can be mathematically modeled as below:

LF ((), w) \approx \frac{1}{w^{τ + 1}}

w = A \times {|B|}^{- ((), \frac{1}{τ})}

σ^{2} = {\{\frac{\sin (π \times τ / 2)}{2^{((), 1 + τ) / 2}} \times \frac{Γ (1 + τ)}{τ \times Γ ((1 + τ) / 2)}\}}^{2 τ^{- 1}}

where τ = 1.5 represents the LF index, w is the step size, A ~ N (0, σ 2 ), B ~ N (0, σ 2 ), Γ(. ) represents the Gamma function, A / B ∼ N (0, σ 2) determines that the samples have been produced by a Gaussian distribution with zero mean value and a variance value σ 2 .…”

Section: Icab Algorithmmentioning

confidence: 99%

“…where τ = 1.5 represents the LF index, 64 w is the step size, A~N(0, σ 2 ), B~N(0, σ 2 ), Γ(.) represents the Gamma function, A/B $ N(0, σ2) determines that the samples have been produced by a Gaussian distribution with zero mean value and a variance value σ 2 .…”

Section: Lévy Flightmentioning

confidence: 99%

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Han

Khodaei

2020

Int J Energy Res

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Summary The aim of this study is to analyze the exergy efficiency of a high‐temperature proton exchange membrane fuel cell. To do this purpose, meta‐heuristic technique has been used. First, the model of a membrane fuel cell is simulated and the polarization diagram shows a potent agreement with empirical data. Then, a new improved version of collective animal behavior algorithm is utilized for evaluating and optimizing the thermodynamic irreversibility, exergy efficiency, and work of the fuel cell. The algorithm uses opposition‐based learning and Lévy flight for improving the algorithm's premature convergence shortcoming. The result of this study shows that by comparison with standard collective animal behavior algorithm, genetic algorithm, and empirical results, the proposed algorithm has better achievements for both terms of optimal value finding and convergence strength.

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“…where τ = 1.5 describes the LF index, 41 w describes the step size, Γ(.) is the Gamma function, A $ N(0, σ 2 ), B $ N (0, σ 2 ), A/B $ N(0, σ 2 ) represents that the samples have been formed by a Gaussian distribution.…”

Section: B Lévy Flight Mechanismmentioning

confidence: 99%

Improved owl search algorithm for optimal capacity determination of the gas engine in aCCHPsystem using4Eanalysis

Cao

Kui

Ashourian

2020

Int Trans Electr Energ Syst

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Combustion optimization of a boiler based on the chaos and Lévy flight vortex search algorithm

Cited by 104 publications

References 23 publications

Using the 0-1 Test for Chaos in Nonlinear Continuous Systems With Two Varying Parameters: Parallel Computations

Using the 0-1 Test for Chaos in Nonlinear Continuous Systems With Two Varying Parameters: Parallel Computations

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Improved owl search algorithm for optimal capacity determination of the gas engine in aCCHPsystem using4Eanalysis

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