Computational Intelligence Optimization And Inverse Problems With Applications In Engineering

This book focuses on metaheuristic methods and its applications to real-world problems in Engineering. The first part describes some key metaheuristic methods, such as Bat Algorithms, Particle Swarm Optimization, Differential Evolution, and Particle Collision Algorithms. Improved versions of these methods and strategies for parameter tuning are also presented, both of which are essential for the practical use of these important computational tools. The second part then applies metaheuristics to problems, mainly in Civil, Mechanical, Chemical, Electrical, and Nuclear Engineering. Other methods, such as the Flower Pollination Algorithm, Symbiotic Organisms Search, Cross-Entropy Algorithm, Artificial Bee Colonies, Population-Based Incremental Learning, Cuckoo Search, and Genetic Algorithms, are also presented. The book is rounded out by recently developed strategies, or hybrid improved versions of existing methods, such as the Lightning Optimization Algorithm, Differential Evolution with Particle Collisions, and Ant Colony Optimization with Dispersion – state-of-the-art approaches for the application of computational intelligence to engineering problems. The wide variety of methods and applications, as well as the original results to problems of practical engineering interest, represent the primary differentiation and distinctive quality of this book. Furthermore, it gathers contributions by authors from four countries – some of which are the original proponents of the methods presented – and 18 research centers around the globe.

This book explores applications of computational intelligence in key and emerging fields of engineering, especially with regard to condition monitoring and fault diagnosis, inverse problems, decision support systems and optimization. These applications can be beneficial in a broad range of contexts, including: water distribution networks, manufacturing systems, production and storage of electrical energy, heat transfer, acoustic levitation, uncertainty and robustness of infinite-dimensional objects, fatigue failure prediction, autonomous navigation, nanotechnology, and the analysis of technological development indexes. All applications, mathematical and computational tools, and original results are presented using rigorous mathematical procedures. Further, the book gathers contributions by respected experts from 22 different research centers and eight countries: Brazil, Cuba, France, Hungary, India, Japan, Romania and Spain. The book is intended for use in graduate courses on applied computation, applied mathematics, and engineering, where tools like computational intelligence and numerical methods are applied to the solution of real-world problems in emerging areas of engineering.

This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer. In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies. From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems. While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding. This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas.

We often come across computational optimization virtually in all branches of engineering and industry. Many engineering problems involve heuristic search and optimization, and, once discretized, may become combinatorial in nature, which gives rise to certain difficulties in terms of solution procedure. Some of these problems have enormous search spaces, are NP-hard and hence require heuristic solution techniques. Another difficulty is the lack of ability of classical solution techniques to determine appropriate optima of non-convex problems. Under these conditions, recent advances in computational optimization techniques have been shown to be advantageous and successful compared to classical approaches. This Volume presents some of the latest developments with a focus on the design of algorithms for computational optimization and their applications in practice. Through the chapters of this book, researchers and practitioners share their experience and newest methodologies with regard to intelligent optimization and provide various case studies of the application of intelligent optimization techniques in real-world applications.This book can serve as an excellent reference for researchers and graduate students in computer science, various engineering disciplines and the industry.

This book brings together a rich selection of studies in mathematical modeling and computational intelligence, with application in several fields of engineering, like automation, biomedical, chemical, civil, electrical, electronic, geophysical and mechanical engineering, on a multidisciplinary approach. Authors from five countries and 16 different research centers contribute with their expertise in both the fundamentals and real problems applications based upon their strong background on modeling and computational intelligence. The reader will find a wide variety of applications, mathematical and computational tools and original results, all presented with rigorous mathematical procedures. This work is intended for use in graduate courses of engineering, applied mathematics and applied computation where tools as mathematical and computational modeling, numerical methods and computational intelligence are applied to the solution of real problems.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

The field of optimization is interdisciplinary in nature, and has been making a significant impact on many disciplines. As a result, it is an indispensable tool for many practitioners in various fields. Conventional optimization techniques have been well established and widely published in many excellent textbooks. However, there are new techniques, such as neural networks, simulated anneal ing, stochastic machines, mean field theory, and genetic algorithms, which have been proven to be effective in solving global optimization problems. This book is intended to provide a technical description on the state-of-the-art development in advanced optimization techniques, specifically heuristic search, neural networks, simulated annealing, stochastic machines, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementa tion, and practical applications. The text is suitable for a first-year graduate course in electrical and computer engineering, computer science, and opera tional research programs. It may also be used as a reference for practicing engineers, scientists, operational researchers, and other specialists. This book is an outgrowth of a couple of special topic courses that we have been teaching for the past five years. In addition, it includes many results from our inter disciplinary research on the topic. The aforementioned advanced optimization techniques have received increasing attention over the last decade, but relatively few books have been produced.

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc. Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems. This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.