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Engineering Systems Optimization



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ISBN 9780815360162
March 15, 2021 Forthcoming by Chapman and Hall/CRC
550 Pages

 
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Book Description

Focuses on system architecture optimization. The numerical algorithms are the core of the book with a brief review for fundamental mathematical concepts. The first two parts include a concise presentation for classical optimization methods. Part three presents details of recent advances in systems architecture optimization. Part four presents detailed engineering applications. The first two parts are suitable for undergraduate engineering students. The whole book is suitable for graduate engineering students and engineers. The numerical algorithms as well as the applications are the core of the book with only a brief review for fundamental mathematical concepts.

Table of Contents

PRELIMINARIES ON OPTIMIZATION. Introduction and Background. Mathematical Foundations. Mathematical Modeling Examples. Linear Optimization. Nonlinear Optimization. CLASSICAL OPTIMIZATION ALGORITHMS. The First Steps. Unconstrained Optimization. Constrained Optimization. SYSTEMS ARCHITECTURE OPTIMIZATION. Global Optimization Algorithms. Hidden Genes Genetic Algorithms. Structured Chromosome Genetic Algorithms. DynamicSizeMultiple Population Genetic Algorithms. APPLICATIONS. Space Trajectory Optimization. Earth Orbiting Satellite Constellation Design. Geometry Optimization of Ocean Wave Energy Converters. Optimal Control. Control Optimization of Wave Energy Converters: A Pseudo Spectral Method. Optimal Control of Wave Energy Converters: Singular Arc Solution

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Author(s)

Biography

Dr. Abdelkhalik conducts research in the area of dynamics, control, and global optimization with applications to spacecraft trajectory planning, wave energy converter control, data assimilation in oil reservoirs, systems design, and traffic engineering. In some applications, the design space has numerous local minima, with mixed variables (integer and real), and the number of optimization variables can be varied among different solutions to explore new regions in the design space. Global optimization methods can handle problems with mixed variables and numerous local minima, but variable-size design space optimization is yet to be explored. The research focus is on the study of global optimization methods that can handle variable-size design space problems. Other research efforts include the recursive implementation of evolutionary optimization algorithms for the sake of improving the computational efficiency in data assimilation problems.