Non Convex Multi Objective Optimization
Download Non Convex Multi Objective Optimization PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Non Convex Multi Objective Optimization book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Non-Convex Multi-Objective Optimization
Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management.
Multi-objective Optimization Techniques
The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms. Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms. Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems. Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms. Discusses multi-objective optimization techniques for cloud, fog, and edge computing. Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer science and engineering, and mathematics.
Geometry and Non-Convex Optimization
This book offers a comprehensive exploration of the dynamic intersection between geometry and optimization. It delves into the intricate study of Hermite-Hadamard inequalities, Hilbert type integral inequalities, and variational inequalities, providing a rich tapestry of theoretical insights and practical applications. Readers will encounter a diverse array of topics, including the bounds for the unweighted Jensen's gap of absolutely continuous functions and the properties of Barrelled and Bornological locally convex spaces. The volume also covers advanced subjects such as multiobjective mixed-integer nonlinear optimization and optimum statistical analysis on sphere surfaces. Contributions from eminent scholars provide a deep dive into C*-ternary biderivations, Erdős-Szekeres products, and variational principles, making this book a must-read for those seeking to expand their understanding of these complex fields. Ideal for researchers and scholars in mathematics and optimization, this volume is an invaluable resource for anyone interested in the latest developments in geometry and nonconvex optimization. Whether you are a seasoned academic or a graduate student, this book will enhance your knowledge and inspire further research in these fascinating domains.