Hasil untuk "Engineering"

D. G. Zill, M. Cullen

Part One - Ordinary Differential Equations. 1. First Order Differential Equations. 2. Second Order Differential Equations. 3. The Laplace Transform. 4. Series Solutions. 5. Numerical Approximation Of Solutions. 6. Sturm-Liouville Theory, Eigenfunction Expansions And Special Functions. Part Two - Vectors And Linear Algebra. 7. Vectors And Vector Space. 8. Matrices, Determinants And Systems Of Linear Equations. 9. Eigenvalues And Diagonalization. Part Three - Systems Of Differential Equations And Qualitative Methods. 10. Linear Systems Of Differential Equations. 11. Non-linear Differential Equations And Qualitative Methods. Part Four - Vector Analysis. 12. Vector Differential Calculus. 13. Vector Integral Calculus. Part Five - Fourier Analysis And Boundary Value Problems. 14. Fourier Series And Integrals. 15. Fourier Transforms. 16. Partial Differential Equations And Boundary Value Problems. Part Six - Complex Analysis. 17. Complex Numbers And Complex Fractions. 18. Complex Integration. 19. Conformal Mappings And Some Applications.

E. C. Alley, Grigory Khimulya, Surojit Biswas et al.

F. Engelmann, Alois Breiing, T. Gutowski

Wei-guo Zhao, Zhenxing Zhang, Liying Wang

Abstract A new bio-inspired optimization technique, named Manta Ray Foraging Optimization (MRFO) algorithm, is proposed and presented, aiming to providing a novel algorithm that provides an alternate optimization approach for addressing real-world engineering issues. The inspiration of this algorithm is based on intelligent behaviors of manta rays. This work mimics three unique foraging strategies of manta rays, including chain foraging, cyclone foraging, and somersault foraging, to develop an efficient optimization paradigm for solving different optimization problems. The performance of MRFO is evaluated, through comparisons with other state-of-the-art optimizers, on benchmark optimization functions and eight real-world engineering design cases. The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors. In addition, the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision. The MATLAB codes of the MRFO algorithm are available at https://www.mathworks.com/matlabcentral/fileexchange/73130-manta-ray-foraging-optimization-mrfo .

Y. S. Zhang, A. Khademhosseini

C. Dym, A. Agogino, O. Eris et al.

N. Barton, R. Lien, J. Lunde

K. Terzaghi, R. Peck, G. Mesri

K. Lee, D. Mooney

K. Rezwan, Qizhi Chen, J. Blaker et al.

M. Vert, Y. Doi, K. Hellwich et al.

G. Desiraju

J. Bond, D. Humphrey, K. Paton et al.

Jakob Nielsen

H. Mark, J. Kroschwitz

I. Hutchings

L. Yang

N. Leveson

G. Tchobanoglous, F. L. Burton

O. Zienkiewicz