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 rese
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Panos M. Pardalos Antanas Žilinskas Julius Žilinskas
Non-Convex MultiObjective Optimization
Springer Optimization and Its Applications Volume 123
Managing Editor Panos M. Pardalos (University of Florida) Editor-Combinatorial Optimization Ding-Zhu Du (University of Texas at Dallas) Advisory Board J. Birge (University of Chicago) C.A. Floudas (Texas A & M University) F. Giannessi (University of Pisa) H.D. Sherali (Virginia Polytechnic and State University) T. Terlaky (Lehigh University) Y. Ye (Stanford University)
Aims and Scope Optimization has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in all areas of applied mathematics, engineering, medicine, economics, and other sciences. The series Springer Optimization and Its Applications publishes undergraduate and graduate textbooks, monographs and state-of-the-art expository work that focus on algorithms for solving optimization problems and also study applications involving such problems. Some of the topics covered include nonlinear optimization (convex and nonconvex), network flow problems, stochastic optimization, optimal control, discrete optimization, multi-objective programming, description of software packages, approximation techniques and heuristic approaches.
More information about this series at http://www.springer.com/series/7393
Panos M. Pardalos • Antanas Žilinskas Julius Žilinskas
Non-Convex Multi-Objective Optimization
123
Panos M. Pardalos Department of Industrial and Systems Engineering University of Florida Gainesville, FL, USA
Antanas Žilinskas Institute of Mathematics & Informatics Vilnius University Vilnius, Lithuania
Research University Higher School of Economics, Russia Julius Žilinskas Institute of Mathematics & Informatics Vilnius University Vilnius, Lithuania
ISSN 1931-6828 ISSN 1931-6836 (electronic) Springer Optimization and Its Applications ISBN 978-3-319-61005-4 ISBN 978-3-319-61007-8 (eBook) DOI 10.1007/978-3-319-61007-8 Library of Congress Control Number: 2017946557 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absen
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