Structural Optimization Using Shuffled Shepherd Meta-Heuristic Algorithm, Ali Kaveh, Ataollah Zaerreza
Автор: Stefan Edelkamp Название: Heuristic Search, ISBN: 0123725127 ISBN-13(EAN): 9780123725127 Издательство: Elsevier Science Рейтинг: Цена: 10004.00 р. 11115.00-10% Наличие на складе: Есть (1 шт.) Описание: Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. This title presents a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems.
Описание: This book focuses on the fields of nature-inspired algorithms, optimization problems and fuzzy logic. In this book, a new metaheuristic based on String Theory from Physics is proposed. It is important to mention that we have proposed the new algorithm to generate new potential solutions in optimization problems in order to find new ways that could improve the results in solving these problems. We are presenting the results for the proposed method in different cases of study. The first case, is optimization of traditional benchmark mathematical functions. The second case, is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting results of the CEC 2017 Competition on Constrained Real-Parameter Optimization that are problems that contain the presence of constraints that alter the shape of the search space making them more difficult to solve. Finally, in the third case, we are presenting the optimization of a fuzzy inference system, specifically for finding the optimal design of a fuzzy controller for an autonomous mobile robot. It is important to mention that in all study cases we are presenting statistical tests in or-der to validate the performance of proposed method. In summary, we believe that this book will be of great interest to a wide audience, ranging from engineering and science graduate students, to researchers and professors in computational intelligence, metaheuristics, optimization, robotics and control.
Описание: This book provides awareness of different evolutionary methods used for automatic generation and optimization of test data in the field of software testing. While the book highlights on the foundations of software testing techniques, it also focuses on contemporary topics for research and development. This book covers the automated process of testing in different levels like unit level, integration level, performance level, evaluation of testing strategies, testing in security level, optimizing test cases using various algorithms, and controlling and monitoring the testing process etc. This book aids young researchers in the field of optimization of automated software testing, provides academics with knowledge on the emerging field of AI in software development, and supports universities, research centers, and industries in new projects using AI in software testing. * Supports the advancement in the artificial intelligence used in software development; * Advances knowledge on artificial intelligence based metaheuristic approach in software testing; * Encourages innovation in traditional software testing field using recent artificial intelligence. ·
Описание: Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis – Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms – Chapters 5-7. Part III: Implementation and application of Deep Statistical Comparison – Chapter 8.
Описание: Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis – Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms – Chapters 5-7. Part III: Implementation and application of Deep Statistical Comparison – Chapter 8.
Название: Meta-heuristic Optimization Techniques ISBN: 3110716178 ISBN-13(EAN): 9783110716177 Издательство: Walter de Gruyter Рейтинг: Цена: 24909.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Without mathematics no science would survive. This especially applies to the engineering sciences which highly depend on the applications of mathematics and mathematical tools such as optimization techniques, finite element methods, differential equations, fluid dynamics, mathematical modelling, and simulation. Neither optimization in engineering, nor the performance of safety-critical system and system security; nor high assurance software architecture and design would be possible without the development of mathematical applications.
De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences (AMEIS) focusses on the latest applications of engineering and information technology that are possible only with the use of mathematical methods. By identifying the gaps in knowledge of engineering applications the AMEIS series fosters the international interchange between the sciences and keeps the reader informed about the latest developments.
Описание: Computer Science and Operations Research continue to have a synergistic relationship and this book - as a part of the Operations Research and Computer Science Interface Series - sits squarely in the center of the confluence of these two technical research communities.
Автор: Dietmar G. Maringer Название: Portfolio Management with Heuristic Optimization ISBN: 1441938427 ISBN-13(EAN): 9781441938428 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The second part (Applications and Contributions) consists of five chapters, covering different problems in financial optimization: the effects of (linear, proportional and combined) transaction costs together with integer constraints and limitations on the initital endowment to be invested;
Автор: Wolfgang Bibel; Pallab Dasgupta; Rudolf Kruse; P. Название: Multiobjective Heuristic Search ISBN: 3528057084 ISBN-13(EAN): 9783528057084 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Solutions to most real-world optimization problems involve a trade-offbetween multiple conflicting and non-commensurate objectives. Some ofthe most challenging ones are area-delay trade-off in VLSI synthesisand design space exploration, time-space trade-off in computation, andmulti-strategy games.
Автор: Taillard, Eric D. Название: Design of heuristic algorithms for hard optimization ISBN: 3031137132 ISBN-13(EAN): 9783031137136 Издательство: Springer Рейтинг: Цена: 5589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods.
Автор: Amitava Chatterjee; Hadi Nobahari; Patrick Siarry Название: Advances in Heuristic Signal Processing and Applications ISBN: 364244525X ISBN-13(EAN): 9783642445255 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems.
In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm intelligence based techniques. The applications considered are in domains such as communications engineering, estimation and tracking, digital filter design, wireless sensor networks, bioelectric signal classification, image denoising, and image feature tracking.
The book presents interesting, state-of-the-art methodologies for solving real-world problems and it is a suitable reference for researchers and engineers in the areas of heuristics and signal processing.
Автор: Amitava Chatterjee; Hadi Nobahari; Patrick Siarry Название: Advances in Heuristic Signal Processing and Applications ISBN: 364237879X ISBN-13(EAN): 9783642378799 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Chap. 1: Nonconvex Optimization via Joint Norm Relaxed SQP and Filled Function Method with Application to Minimax Two-Channel Linear Phase FIR QMF Bank Design.- Chap. 2: Robust Reduced-Rank Adaptive LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Parameters.- Chap. 3: Designing OFDM Radar Waveform for Target Detection Using Multiobjective Optimization.- Chap. 4: Multiobject Tracking using Particle Swarm Optimization on Target Interactions.- Chap. 5: A Comparative Study of Modified BBO Variants and Other Metaheuristics for Optimal Power Allocation in Wireless Sensor Networks.- Chap. 6: Joint Optimization of Detection and Tracking in Adaptive Radar Systems.- Chap. 7: Iterative Design of FIR Filters.- Chap. 8: A Metaheuristic Approach to Two-Dimensional Recursive Digital Filter Design.- Chap. 9: A Survey of Kurtosis Optimization Schemes for MISO Source Separation and Equalization.- Chap. 10: Swarm Intelligence Techniques Applied to Nonlinear Systems State Estimation.- Chap. 11: Heuristic Optimal Design of Multiplier-less Digital Filter.- Chap. 12: Hybrid Correlation-Neural Network Synergy for Gait Signal Classification.- Chap. 13: Image Denoising Using Wavelets: Application in Medical Imaging.- Chap. 14: Signal Separation with A Priori Knowledge Using Sparse Representation.- Chap. 15: Definition of a Discrete Color Monogenic Wavelet Transform.- Chap. 16: On Image Matching and Feature Tracking for Embedded Systems: State of the Art.
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