[PDF] Optimization Techniques For Problem Solving In Uncertainty eBook

Author : Tilahun, Surafel Luleseged
Publisher : IGI Global
Page : 327 pages
File Size : 44,54 MB
Release : 2018-06-22
Category : Computers
ISBN : 1522550925

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When it comes to optimization techniques, in some cases, the available information from real models may not be enough to construct either a probability distribution or a membership function for problem solving. In such cases, there are various theories that can be used to quantify the uncertain aspects. Optimization Techniques for Problem Solving in Uncertainty is a scholarly reference resource that looks at uncertain aspects involved in different disciplines and applications. Featuring coverage on a wide range of topics including uncertain preference, fuzzy multilevel programming, and metaheuristic applications, this book is geared towards engineers, managers, researchers, and post-graduate students seeking emerging research in the field of optimization.

Author : Saxena, Pratiksha
Publisher : IGI Global
Page : 424 pages
File Size : 14,97 MB
Release : 2016-03-01
Category : Mathematics
ISBN : 1466698861

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Optimization techniques have developed into a modern-day solution for real-world problems in various industries. As a way to improve performance and handle issues of uncertainty, optimization research becomes a topic of special interest across disciplines. Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications presents the latest research trends and developments in the area of applied optimization methodologies and soft computing techniques for solving complex problems. Taking a multi-disciplinary approach, this critical publication is an essential reference source for engineers, managers, researchers, and post-graduate students.

Author : Giuseppe Calafiore
Publisher : Springer Science & Business Media
Page : 454 pages
File Size : 38,43 MB
Release : 2006-03-06
Category : Technology & Engineering
ISBN : 1846280958

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Probabilistic and Randomized Methods for Design under Uncertainty is a collection of contributions from the world’s leading experts in a fast-emerging branch of control engineering and operations research. The book will be bought by university researchers and lecturers along with graduate students in control engineering and operational research.

Author : Richard Bellman
Publisher :
Page : 362 pages
File Size : 25,33 MB
Release : 2012-07-01
Category :
ISBN : 9781258437640

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Contributing Authors Include Angelo Miele, P. Dergarabedian, R. P. Ten Dyke, And Many Others.

Author : Casey Vi Horgan
Publisher :
Page : 132 pages
File Size : 33,36 MB
Release : 2017
Category :
ISBN :

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Uncertainty is often present in real-life problems. Deciding how to deal with this uncertainty can be difficult. The proper formulation of a problem can be the larger part of the work required to solve it. This thesis is intended to be used by a decision maker to determine how best to formulate a problem. Robust optimization and partially observable Markov decision processes (POMDPs) are two methods of dealing with uncertainty in real life problems. Robust optimization is used primarily in operations research, while engineers will be more familiar with POMDPs. For a decision maker who is unfamiliar with one or both of these methods, this thesis will provide insight into a different way of problem solving in the presence of uncertainty. The formulation of each method is explained in detail, and the theory of common solution methods is presented. In addition, several examples are given for each method. While a decision maker may try to solve an entire problem using one method, sometimes there are natural partitions to a problem that encourage using multiple solution methods. In this thesis, one such problem is presented, a military planing problem consisting of two parts. The first part is best solved with POMDPs and the second with robust optimization. The reasoning behind this partition is explained and the formulation of each part is presented. Finally, a discussion of the problem types suitable for each method, including multiple applications, is provided.

Author : Günter Rudolph
Publisher : Springer Nature
Page : 632 pages
File Size : 47,43 MB
Release : 2022-08-13
Category : Computers
ISBN : 3031147146

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This two-volume set LNCS 13398 and LNCS 13399 constitutes the refereed proceedings of the 17th International Conference on Parallel Problem Solving from Nature, PPSN 2022, held in Dortmund, Germany, in September 2022. The 87 revised full papers were carefully reviewed and selected from numerous submissions. The conference presents a study of computing methods derived from natural models. Amorphous Computing, Artificial Life, Artificial Ant Systems, Artificial Immune Systems, Artificial Neural Networks, Cellular Automata, Evolutionary Computation, Swarm Computing, Self-Organizing Systems, Chemical Computation, Molecular Computation, Quantum Computation, Machine Learning, and Artificial Intelligence approaches using Natural Computing methods are just some of the topics covered in this field.