Optimization techniques in engineering : advances and applications - Khosla, Anita (Hoboken, NJ : John Wiley & Sons, Incorporated, 2023)
OPTIMIZATION TECHNIQUES IN ENGINEERING The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained. This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I -- Soft Computing and Evolutionary-Based Optimization; and Part II -- Decision Science and Simulation-Based Optimization, which contains application-based chapters. Readers and users will find in the book: An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics; An in-depth treatment of contributions to optimal learning and optimizing engineering systems; Maps out the relations between optimization and other mathematical topics and disciplines; A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems. Audience Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.
12.3.1 Decision Variables -- 12.3.2 Objective Functions -- 12.3.3 Bounds of Decision Variables -- 12.3.4 Response Variables -- 12.4 Results and Discussions -- 12.4.1 Single Objective Optimization -- 12.4.2 Results of Multiobjective Optimization -- 12.5 Conclusion -- References -- Chapter 13 Genetic Algorithm-Based Optimization for Speech Processing Applications -- 13.1 Introduction to GA -- 13.1.1 Enhanced GA -- 13.2 GA in Automatic Speech Recognition -- 13.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD) -- 13.2.2 Classification of Features in ASR Using GA -- 13.2.3 GA-Based Distinctive Phonetic Features Recognition -- 13.2.4 GA in Phonetic Decoding -- 13.3 Genetic Algorithm in Speech Emotion Recognition -- 13.3.1 Speech Emotion Recognition -- 13.3.2 Genetic Algorithms in Speech Emotion Recognition -- 13.4 Genetic Programming in Hate Speech Using Deep Learning -- 13.4.1 Introduction to Hate Speech Detection -- 13.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection -- 13.5 Conclusion -- References -- Chapter 14 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant -- 14.1 Introduction -- 14.2 Single-Area Power System -- 14.3 Automatic Load Frequency Control (ALFC) -- 14.4 Controllers Used in the Simulink Model -- 14.4.1 PID Controller -- 14.4.2 PI Controller -- 14.4.3 P Controller -- 14.5 Circuit Description -- 14.6 ANN and NARMA L2 Controller -- 14.7 Simulation Results and Comparative Analysis -- 14.8 Conclusion -- References -- Part 2: Decision Science and Simulation-Based Optimization -- Chapter 15 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment -- 15.1 Introduction -- 15.2 Fuzzy Set Theory -- 15.2.1 Some Important Fuzzy Definitions -- 15.2.2 Fuzzy Operations.
Description based on online resource; title from digital title page (viewed on May 05, 2023).
John Wiley and Sons
Keywords: Engineering -- Mathematical models; x; Engineering -- Mathematical models