Articles by "Optimization"

Showing posts with label Optimization. Show all posts

Marco Locatelli, Fabio Schoen ... 445 pages - Publisher: SIAM-Society for Industrial and Applied Mathematics (October, 2013) ... Language: English - ISBN-10: 1611972663 - ISBN-13: 978-1611972665.

This volume contains a thorough overview of the rapidly growing field of global optimization, with chapters on key topics such as complexity, heuristic methods, derivation of lower bounds for minimization problems, and branch-and-bound methods and convergence. The final chapter offers both benchmark test problems and applications of global optimization, such as finding the conformation of a molecule or planning an optimal trajectory for interplanetary space travel. An appendix provides fundamental information on convex and concave functions.

Audience: Global Optimization is intended for Ph.D. students, researchers, and practitioners looking for advanced solution methods to difficult optimization problems. It can be used as a supplementary text in an advanced graduate-level seminar. Contents: Chapter 1: Introduction; Chapter 2: Complexity; Chapter 3: Heuristics; Chapter 4: Lower Bounds; Chapter 5: Branch and Bound; Chapter 6: Problems; Appendix A: Basic Definitions and Results on Convexity; Appendix B: Notation.

Pedro Ponce-Cruz, Arturo Molina Gutiérrez, Ricardo A. Ramírez-Mendoza, Efraín Méndez Flores, Alexandro Antonio Ortiz Espinoza, David Christopher Balderas Silva ... 178 pages - Publisher: Chapman and Hall/CRC; (June, 2020) ... Language: English - AmazonSIN: B08LGNKST8.

Metaheuristic optimization has become a prime alternative for solving complex optimization problems in several areas. Hence, practitioners and researchers have been paying extensive attention to those metaheuristic algorithms that are mainly based on natural phenomena. However, when those algorithms are implemented, there are not enough books that deal with theoretical and experimental problems in a friendly manner so this book presents a novel structure that includes a complete description of the most important metaheuristic optimization algorithms as well as a new proposal of a new metaheuristic optimization named earthquake optimization. This book also has several practical exercises and a toolbox for MATLAB® and a toolkit for LabVIEW are integrated as complementary material for this book. These toolkits allow readers to move from a simulation environment to an experimentation one very fast. This book is suitable for researchers, students, and professionals in several areas, such as economics, architecture, computer science, electrical engineering, and control systems.

The unique features of this book are as follows: Developed for researchers, undergraduate and graduate students, and practitioners + A friendly description of the main metaheuristic optimization algorithms + Theoretical and practical optimization examples + A new earthquake optimization algorithm + Updated state-of-the-art and research optimization projects.

João Paulo Coelho, Tatiana M. Pinho, José Boaventura-Cunha ... 296 pages - ISBN-13: 978-0367203498 ... Publisher: CRC Press; (August, 2019) - Language: English.

This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models’ concepts from the domain of formal mathematics into computer codes using MATLAB. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the description of the fundamental algorithms behind hidden Markov models using MATLAB. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach.

Key Selling Points: Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory + Covers the analysis of both continuous and discrete Markov chains + Discusses the translation of HMM concepts from the realm of formal mathematics into computer code + Offers many examples to supplement mathematical notation when explaining new concepts.

Shun-Zheng Yu ... 208 pages - AmazonSIN: B017A382CC ... Language: English - Publisher: Elsevier; (October, 2015).

Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.

Discusses the latest developments and emerging topics in the field of HSMMs + Includes a description of applications in various areas including, Human Activity Recognition, Handwriting Recognition, Network + Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping. + Shows how to master the basic techniques needed for using HSMMs and how to apply them.

David R. Westhead, M. S. Vijayabaskar ... 231 pages - ISBN-13: 978-1493967513 ... Publisher: Humana; (February, 2017) - Language: English - AmazonSIN: 1493967517.

This volume aims to provide a new perspective on the broader usage of Hidden Markov Models (HMMs) in biology. Hidden Markov Models: Methods and Protocols guides readers through chapters on biological systems; ranging from single biomolecule, cellular level, and to organism level and the use of HMMs in unravelling the complex mechanisms that govern these complex systems. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. 

Authoritative and practical, Hidden Markov Models: Methods and Protocols aims to demonstrate the impact of HMM in biology and inspire new research.

Kaushik Kumar, J. Paulo Davim ... 179 pages - AmazonSIN: B07V7SN988 ... Publisher: Wiley-ISTE; (July, 2019) - Language: English.

Optimization is central to any problem involving decision-making in engineering. Optimization theory and methods deal with selecting the best option regarding the given objective function or performance index. New algorithmic and theoretical techniques have been developed for this purpose, and have rapidly diffused into other disciplines. As a result, our knowledge of all aspects of the field has grown even more profound. In Optimization for Engineering Problems, eminent researchers in the field present the latest knowledge and techniques on the subject of optimization in engineering. Whereas the majority of work in this area focuses on other applications, this book applies advanced and algorithm-based optimization techniques specifically to problems in engineering.

Taufik Abrão ... 292 pages - Publisher: (February, 2013) ... Language: English - ISBN-13: 978-9535109839.

Heuristic Search is an important sub-discipline of optimization theory and finds applications in a vast variety of fields, including life science and engineering. Search methods have been useful in solving tough engineering-oriented problems that either could not be solved any other way or solutions take a very long time to be computed. This book explores a variety of applications for search methods and techniques in different fields of electrical engineering. By organizing relevant results and applications, this book will serve as a useful resource for students, researchers and practitioners to further exploit the potential of search methods in solving hard optimization problems that arise in advanced engineering technologies, such as image and video processing issues, detection and resource allocation in telecommunication systems, security and harmonic reduction in power generation systems, as well as redundancy optimization problem and search-fuzzy learning mechanisms in industrial applications.

Lindo What'sBest! v17.0.0.0 x64 [Size: 57.7 MB] ... What'sBest! is an add-in to Excel that allows you to build large scale optimization models in a free form layout within a spreadsheet. What'sBest! combines the proven power of Linear, Nonlinear (convex and nonconvex/Global), Quadratic, Quadratically Constrained, Second Order Cone, Semi-Definite, Stochastic, and Integer optimization with Microsoft Excel - the most popular and flexible business modeling environment in use today. The recently released What'sBest! 15.0 includes a number of significant enhancements and new features. Enhancements to the Simplex solvers boost performance on linear models. Large models solve an average of 20% faster using primal simplex and 15% faster for dual simplex. New symmetry detection capabilities dramatically reduce the time required to prove optimality on certain classes of models with integer variables. Performance has been improved on Markowitz portfolio problems with minimum buy quantities, and/or limit on number of instruments at nonzero level. Other enhancements provide faster solutions on certain task assignment-like models. Stability and robustness of the Global solver has been improved through several enhancements to quadratic recognition and range reduction. Improved exploitation of convexity of certain ratio constraints, e.g., as found in heat exchanger network design problems. Several new functions and constraint types are recognized, e.g., the =WBALLDIFF() All Different constraint, for general integer variables. The =WBALLDIFF() function allows one to specify a set of integer variables, such that each variable in the set must have a unique value, different from all other variables in the set.

Mykel J. Kochenderfer, Tim A. Wheeler ... 520 pages - Publisher: The MIT Press; (March, 2019) ... Language: English - ISBN-10: 0262039427 - ISBN-13: 978-0262039420.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems: This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

Naiyang Deng, Yingjie Tian, Chunhua Zhang ... 363 pages - Publisher: Chapman and Hall/CRC; (December, 2012) ... Language: English - AmazonSIN: B00OD4GXCI.

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations. To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.

Anand J. Kulkarni, Suresh Chandra Satapathy ... 197 pages - Publisher: Springer; (November, 2019) ... Language: English - AmazonSIN: B0825P5H9C.

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.

Fouad Bennis, Rajib Kumar Bhattacharjya ... 502 pages - Publisher: Springer; (January, 2020) ... Language: English - ASIN: B0843NX5CT.

This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.

Mircea Ancau ... 295 pages - Publisher: Cambridge Scholars Publishing; (November, 2019) ... Language: English - ISBN-10: 1527538494 - ISBN-13: 978-1527538498.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems: This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.

Nilanjan Dey ... 266 pages - Publisher: Springer; (November, 2019) ... Language: English - AmazonSIN: B0818MWNQJ.

The book discusses advantages of the firefly algorithm over other well-known metaheuristic algorithms in various engineering studies. The book provides a brief outline of various application-oriented problem solving methods, like economic emission load dispatch problem, designing a fully digital controlled reconfigurable switched beam nonconcentric ring array antenna, image segmentation, span minimization in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use of the firefly algorithm to select features, as research has shown that the firefly algorithm generates precise and optimal results in terms of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman problem, graph coloring problem, etc.

Ivan Nunes da Silva, Danilo Hernane Spatti ... 307 pages - Publisher: Springer; (August, 2016) - Language: English - ISBN-10: 3319431617 - ISBN-13: 978-3319431611 ...

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

I’m very glad to have opportunity to teach you one of the most popular and powerful optimization algorithms in this course.

If you search FireFly optimization algorithm in google scholar, it could be seen that there are many vast range of papers has been published by implementing this optimization algorithm in different fields of science. In this course, after presenting the mathematical concept of each part of the considered optimization algorithm, I write its code immediately in matlab. All of the written codes are available, however, I strongly suggest to write the codes with me. Notice that, if you don’t have matlab or you know another programming language, don’t worry at all. You can simply write the codes in your own programming language because the behind concepts about all of the written codes are presented completely.

Rajesh Kumar Arora ... 466 pages - Publisher: Chapman and Hall/CRC; (May, 2015) ... Language: English - ISBN-10: 1498721125 - ISBN-13: 978-1498721127.

Choose the Correct Solution Method for Your Optimization Problem: Optimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden–Fletcher–Goldfarb–Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible directions, genetic algorithms, particle swarm optimization (PSO), simulated annealing, ant colony optimization, and tabu search methods. The author shows how to solve non-convex multi-objective optimization problems using simple modifications of the basic PSO code. The book also introduces multidisciplinary design optimization (MDO) architectures―one of the first optimization books to do so―and develops software codes for the simplex method and affine-scaling interior point method for solving linear programming problems. In addition, it examines Gomory’s cutting plane method, the branch-and-bound method, and Balas’ algorithm for integer programming problems. The author follows a step-by-step approach to developing the MATLAB® codes from the algorithms. He then applies the codes to solve both standard functions taken from the literature and real-world applications, including a complex trajectory design problem of a robot, a portfolio optimization problem, and a multi-objective shape optimization problem of a reentry body. This hands-on approach improves your understanding and confidence in handling different solution methods. The MATLAB codes are available on the book’s CRC Press web page.

Yogesh Jalria ... 614 pages - Publisher: CRC Press; 3rd edition (September, 2019) ... Language: English - ISBN-10: 1498778232 - ISBN-13: 978-1498778237.

Design and Optimization of Thermal Systems, Third Edition: with MATLAB® Applications provides systematic and efficient approaches to the design of thermal systems, which are of interest in a wide range of applications. It presents basic concepts and procedures for conceptual design, problem formulation, modeling, simulation, design evaluation, achieving feasible design, and optimization. Emphasizing modeling and simulation, with experimentation for physical insight and model validation, the third edition covers the areas of material selection, manufacturability, economic aspects, sensitivity, genetic and gradient search methods, knowledge-based design methodology, uncertainty, and other aspects that arise in practical situations. This edition features many new and revised examples and problems from diverse application areas and more extensive coverage of analysis and simulation with MATLAB®.

Seyedali Mirjalili, Jin Song Dong ... 72 pages - Publisher: Springer; (July, 2019) ... Language: English - ASIN: B07VLFKLHW by Amazon.

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Krishnanand N. Kaipa, Debasish Ghose ... 248 pages - Publisher: Springer; (January, 2017) ... Language: English - ASIN: B01N7R22Z8 by Amazon.

This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.

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