Engineering Optimization: Methods and Applications – serie

This book discusses generalized applications of energy storage systems using experimental, numerical, analytical, and optimization approaches. The book includes novel and hybrid optimization techniques developed for energy storage systems. It provides a range of applications of energy storage systems on a single platform. The book broadly covers—thermal management of electronic components in portable electronic devices; modeling and optimization aspects of energy storage systems; management of power generation systems involving renewable energy; testing, evaluation, and life cycle assessment of energy storage systems, etc. This book will serve as a reference resource for researchers and practitioners in academia and industry.

This book discusses generalized applications of energy storage systems using experimental, numerical, analytical, and optimization approaches. The book includes novel and hybrid optimization techniques developed for energy storage systems. It provides a range of applications of energy storage systems on a single platform. The book broadly covers—thermal management of electronic components in portable electronic devices; modeling and optimization aspects of energy storage systems; management of power generation systems involving renewable energy; testing, evaluation, and life cycle assessment of energy storage systems, etc. This book will serve as a reference resource for researchers and practitioners in academia and industry.

This book covers the latest advances in Cultural Algorithms, their general framework, different variants, hybridized versions with other meta-heuristic and search techniques, and their applications. Cultural Algorithms are meta-heuristic numerical optimization techniques inspired by the bio-cultural evolutionary theory, in which both types of vertical and horizontal learning behaviors are modeled. The book includes well-briefed basics of optimization and theoretical backgrounds of Cultural Algorithms in its initial chapters and then discusses their applications in different branches of science and engineering. It provides detailed mathematical formulations and algorithmic pseudo-codes of hybridized, extended, and multi-population variants of cultural algorithms. The book will serve the research students, fellows, professors, and industry professionals to implement real-time applications of Cultural Algorithms.

This book covers the latest advances in Cultural Algorithms, their general framework, different variants, hybridized versions with other meta-heuristic and search techniques, and their applications. Cultural Algorithms are meta-heuristic numerical optimization techniques inspired by the bio-cultural evolutionary theory, in which both types of vertical and horizontal learning behaviors are modeled. The book includes well-briefed basics of optimization and theoretical backgrounds of Cultural Algorithms in its initial chapters and then discusses their applications in different branches of science and engineering. It provides detailed mathematical formulations and algorithmic pseudo-codes of hybridized, extended, and multi-population variants of cultural algorithms. The book will serve the research students, fellows, professors, and industry professionals to implement real-time applications of Cultural Algorithms.

The book explores the latest developments in Operations Research and Artificial Intelligence and their transformative impact on healthcare systems and supply chains. It takes an in-depth look at emerging trends and their practical applications in critical areas such as healthcare delivery and supply chain management. Readers will discover how the integration of Operations Research and Artificial Intelligence can revolutionize decision-making, optimize resource allocation in healthcare, streamline patient flow, and enhance overall operational efficiency. The book also examines how these techniques are reshaping inventory management, procurement strategies, and the timely delivery of healthcare resources within supply chains. Packed with real-world case studies and practical examples, this book provides actionable insights into successfully implementing Operations Research and Artificial Intelligence techniques. It is designed for a broad audience, including researchers, healthcare professionals, supply chain experts, and students eager to stay ahead of the latest trends in these fields.This book equips readers with the knowledge and tools to overcome challenges and seize new opportunities. It aims to keep readers informed about cutting-edge innovations and help them understand the transformative potential of Operations Research and Artificial Intelligence in healthcare and supply chain management.

The book's objective is to develop mathematical structures that can be applied to real-life problems with sustainable goals. It focuses on the impact of sustainable living on social, economic, and environmental aspects, aiming to create optimization techniques that minimize emissions and maximize green energy. These optimization problems may include sustainable transport, cities, economic development, living, and tourism. The book brings together researchers, academics, and professionals from various fields to find optimal or satisfactory solutions for various environmentally friendly sustainable problems. It aims to achieve this not only with existing optimization techniques, but also with novel approaches such as lexicographic optimization, heuristic approaches, DEA, and genetic algorithms. The goal is to develop practical algorithms and methods applicable to these problems under uncertain circumstances and explore the potential for improving the efficiency of existing algorithms.

The book's objective is to develop mathematical structures that can be applied to real-life problems with sustainable goals. It focuses on the impact of sustainable living on social, economic, and environmental aspects, aiming to create optimization techniques that minimize emissions and maximize green energy. These optimization problems may include sustainable transport, cities, economic development, living, and tourism. The book brings together researchers, academics, and professionals from various fields to find optimal or satisfactory solutions for various environmentally friendly sustainable problems. It aims to achieve this not only with existing optimization techniques, but also with novel approaches such as lexicographic optimization, heuristic approaches, DEA, and genetic algorithms. The goal is to develop practical algorithms and methods applicable to these problems under uncertain circumstances and explore the potential for improving the efficiency of existing algorithms.

This book explores the development of several new learning algorithms that utilize recent optimization techniques and meta-heuristics. It addresses well-known models such as particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, population-based incremental learning, and grey wolf optimizer for training neural networks. Additionally, the book examines the challenges associated with these processes in detail. This volume will serve as a valuable reference for individuals in both academia and industry.

This book explores the development of several new learning algorithms that utilize recent optimization techniques and meta-heuristics. It addresses well-known models such as particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, population-based incremental learning, and grey wolf optimizer for training neural networks. Additionally, the book examines the challenges associated with these processes in detail. This volume will serve as a valuable reference for individuals in both academia and industry.

The book includes nature-inspired optimization techniques and their applications. It offers recent trends in the field of nature-inspired algorithms for solving real-life problems in various fields related to manufacturing, artificial intelligence, finance, etc. Nature-inspired optimization techniques are not only useful but also needed for solving open-ended problems. Understanding nature-inspired techniques and their importance for solving real-life problems can open many directions for researchers and academicians. This book will be helpful in acquiring knowledge of nature-inspired optimization techniques in various fields of real-life applications.

The book includes nature-inspired optimization techniques and their applications. It offers recent trends in the field of nature-inspired algorithms for solving real-life problems in various fields related to manufacturing, artificial intelligence, finance, etc. Nature-inspired optimization techniques are not only useful but also needed for solving open-ended problems. Understanding nature-inspired techniques and their importance for solving real-life problems can open many directions for researchers and academicians. This book will be helpful in acquiring knowledge of nature-inspired optimization techniques in various fields of real-life applications.

This book provides a comprehensive review of the latest developments in optimization based learning algorithms within the field of electrical engineering. It covers various power system applications including efficient power system operation, load forecasting, fault analysis, home automation and efficient smart grid management. Each application is accompanied by case studies and a literature review in self-contained chapters. The book is dedicated to study the effectiveness of intelligent methods in addressing the power system problems and its mitigation using optimization algorithms. It discusses several optimization algorithms such as random forest algorithm, metaheuristic algorithm, scaled conjugate gradient descent algorithm, artificial bee colony algorithm etc. and their usability in intelligent decision makers for the various optimization problems in electrical engineering. This timely book serves as a practical guide and reference sources for students, researchers and professionals.

This book provides a comprehensive review of the latest developments in optimization based learning algorithms within the field of electrical engineering. It covers various power system applications including efficient power system operation, load forecasting, fault analysis, home automation and efficient smart grid management. Each application is accompanied by case studies and a literature review in self-contained chapters. The book is dedicated to study the effectiveness of intelligent methods in addressing the power system problems and its mitigation using optimization algorithms. It discusses several optimization algorithms such as random forest algorithm, metaheuristic algorithm, scaled conjugate gradient descent algorithm, artificial bee colony algorithm etc. and their usability in intelligent decision makers for the various optimization problems in electrical engineering. This timely book serves as a practical guide and reference sources for students, researchers and professionals.

This book presents state-of-the-art technologies in wind farm layout optimization and control to improve the current industry/research practice. The contents take readers towards a different kind of uncertainty handling through the discussion on several techniques enabling maximum energy harnessing out of uncertain situations. The book aims to give a detailed overview of such concepts in the first part, where the recent advancements in the fields of (i) Wind farm layout optimization, (ii) Multi-objective Optimization and Uncertainty handling in optimization methods, (iii) Development of Machine Learning-based surrogate models in optimization, and (iv) Different types of wake models for wind farms will be discussed. The second part will cover the application of the aforementioned techniques on the wind farm layout optimization and control through several chapters such as (i) Wind farm performance assessment using Computational Fluid Dynamics (CFD) tools, (ii) Artificial Neural Network (ANN) based hybrid wake models, (iii) Long Short-term Memory (LSTM) & Support Vector Regression (SVR) based forecasting and micro-siting, (iv) windfarm micro-siting using data-driven Robust Optimization (RO) as well as Generative Adversarial Networks (GANs), (v) Reinforcement learning (RL) based wind farm control and (vi) Application of eXplainable AI (XAI) tools for interpreting wind time-series data. In this manner, the book provides state-of-the-art techniques in the fields of multi-objective optimization, Evolutionary Algorithms, Machine Learning surrogate models, Bayesian Optimization, Data Analysis, and Optimization under Uncertainty and their applications in the field of wind energy generation that can be extremely generic and can be applied to many other engineering fields. This volume will be of interest to those in academia and industry.

This book presents state-of-the-art technologies in wind farm layout optimization and control to improve the current industry/research practice. The contents take readers towards a different kind of uncertainty handling through the discussion on several techniques enabling maximum energy harnessing out of uncertain situations. The book aims to give a detailed overview of such concepts in the first part, where the recent advancements in the fields of (i) Wind farm layout optimization, (ii) Multi-objective Optimization and Uncertainty handling in optimization methods, (iii) Development of Machine Learning-based surrogate models in optimization, and (iv) Different types of wake models for wind farms will be discussed. The second part will cover the application of the aforementioned techniques on the wind farm layout optimization and control through several chapters such as (i) Wind farm performance assessment using Computational Fluid Dynamics (CFD) tools, (ii) Artificial Neural Network (ANN) based hybrid wake models, (iii) Long Short-term Memory (LSTM) & Support Vector Regression (SVR) based forecasting and micro-siting, (iv) windfarm micro-siting using data-driven Robust Optimization (RO) as well as Generative Adversarial Networks (GANs), (v) Reinforcement learning (RL) based wind farm control and (vi) Application of eXplainable AI (XAI) tools for interpreting wind time-series data. In this manner, the book provides state-of-the-art techniques in the fields of multi-objective optimization, Evolutionary Algorithms, Machine Learning surrogate models, Bayesian Optimization, Data Analysis, and Optimization under Uncertainty and their applications in the field of wind energy generation that can be extremely generic and can be applied to many other engineering fields. This volume will be of interest to those in academia and industry.

This book concisely presents the optimization process and optimal control process with examples and simulations to help self-learning and better comprehension. It starts with function optimization and constraint inclusion and then extends to functional optimization using the calculus of variations. The development of optimal controls for continuous-time, linear, open-loop systems is presented using Lagrangian and Pontryagin-Hamiltonian methods, showing how to introduce the end-point conditions in time and state. The closed-loop optimal control for linear systems with a quadratic cost function, well-known as the linear quadratic regulator (LQR) is developed for both time-bound and time-unbounded conditions. Some control systems need to maximize performance alongside cost minimization. The Pontryagin's maximum principle is presented in this regard with clear examples that show the practical implementation of it. It is shown through examples how the maximum principle leads to control switching and Bang-Bang control in certain types of systems. The application of optimal controls in discrete-time open-loop systems with the quadratic cost is presented and then extended to the closed-loop control, which results in the model predictive control (MPC). Throughout the book, examples and Matlab simulation codes are provided for the learner to practice the contents in each section. The aligned lineup of content helps the learner develop knowledge and skills in optimal control gradually and quickly.

This book concisely presents the optimization process and optimal control process with examples and simulations to help self-learning and better comprehension. It starts with function optimization and constraint inclusion and then extends to functional optimization using the calculus of variations. The development of optimal controls for continuous-time, linear, open-loop systems is presented using Lagrangian and Pontryagin-Hamiltonian methods, showing how to introduce the end-point conditions in time and state. The closed-loop optimal control for linear systems with a quadratic cost function, well-known as the linear quadratic regulator (LQR) is developed for both time-bound and time-unbounded conditions. Some control systems need to maximize performance alongside cost minimization. The Pontryagin's maximum principle is presented in this regard with clear examples that show the practical implementation of it. It is shown through examples how the maximum principle leads to control switching and Bang-Bang control in certain types of systems. The application of optimal controls in discrete-time open-loop systems with the quadratic cost is presented and then extended to the closed-loop control, which results in the model predictive control (MPC). Throughout the book, examples and Matlab simulation codes are provided for the learner to practice the contents in each section. The aligned lineup of content helps the learner develop knowledge and skills in optimal control gradually and quickly.

This edited book provides a platform to discuss the state-of-the-art developments associated with traditional and advanced single-/multi-objective criteria optimization methods for addressing problems of performance enhancement of the products and systems design. The book in detail discusses the core ideas, underlying principles, mathematical formulations, critical reviews and experimentations, and solutions to complex problems from within the domains such as mechanical engineering design and manufacturing, fault detection and diagnosis, control systems, financial systems, machine learning in medical image processing as well as problems from operations research domain. It will serve as a valuable reference to academicians and industry practitioners involved in improving the efficiency, cost, performance, and durability of the products and systems. The chapters in this book may further give impetus to explore new avenues leading towards multidisciplinary research discussions associatedwith the resilience and sustainability of the existing systems.

This edited book provides a platform to discuss the state-of-the-art developments associated with traditional and advanced single-/multi-objective criteria optimization methods for addressing problems of performance enhancement of the products and systems design. The book in detail discusses the core ideas, underlying principles, mathematical formulations, critical reviews and experimentations, and solutions to complex problems from within the domains such as mechanical engineering design and manufacturing, fault detection and diagnosis, control systems, financial systems, machine learning in medical image processing as well as problems from operations research domain. It will serve as a valuable reference to academicians and industry practitioners involved in improving the efficiency, cost, performance, and durability of the products and systems. The chapters in this book may further give impetus to explore new avenues leading towards multidisciplinary research discussions associatedwith the resilience and sustainability of the existing systems.

This contributed book focuses on optimization methods inspired by nature such as Harmony Search Algorithm, Drosophila Food-Search Algorithm, Cohort intelligence algorithm and its variations, fuzzy logic along with their hybridization variants. It also focuses on multi-objective optimization algorithms such as Non-Dominated Sorting Genetic Algorithm, Particle Swarm Optimization, Evolutionary Algorithm, Pareto Envelope Selection Algorithm, and Strength Pareto Evolutionary Algorithm. The content focuses on topics such as the optimal design of truss systems with various applications, the design and simulation of quarter car systems for comfort design, the road handling design and a balanced system, and topology optimization of 2-dimensional and 3-dimensional structure in linear elasticity, plasticity and fracture mechanics among others. This book is a useful reference for those in academia and industry.

This contributed book focuses on optimization methods inspired by nature such as Harmony Search Algorithm, Drosophila Food-Search Algorithm, Cohort intelligence algorithm and its variations, fuzzy logic along with their hybridization variants. It also focuses on multi-objective optimization algorithms such as Non-Dominated Sorting Genetic Algorithm, Particle Swarm Optimization, Evolutionary Algorithm, Pareto Envelope Selection Algorithm, and Strength Pareto Evolutionary Algorithm. The content focuses on topics such as the optimal design of truss systems with various applications, the design and simulation of quarter car systems for comfort design, the road handling design and a balanced system, and topology optimization of 2-dimensional and 3-dimensional structure in linear elasticity, plasticity and fracture mechanics among others. This book is a useful reference for those in academia and industry.

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to machine learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, machine learning, artificial intelligence, modified/newly developed machine learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here.