Ravipudi Venkata Rao – författare

Optimization Methods for Finite Element Analysis and Design describes recent developments in Finite Element Methods (FEM). It gives a brief introduction of the applications of AI-based nature-inspired metaheuristic algorithms and machine learning (ML) at various stages of FEM. The book covers a range of state-of-the-art application areas including medical equipment, structural analysis and machinery products.

It explores the applications of optimization and ML techniques in mesh smoothing, quality improvement and Laplacian and Taubin smoothing. The book also discusses the optimization of cable nets and steel frames using nature-inspired metaheuristic methods.

Optimization Methods for Finite Element Analysis and Design describes recent developments in Finite Element Methods (FEM). It gives a brief introduction of the applications of AI-based nature-inspired metaheuristic algorithms and machine learning (ML) at various stages of FEM. The book covers a range of state-of-the-art application areas including medical equipment, structural analysis and machinery products.

It explores the applications of optimization and ML techniques in mesh smoothing, quality improvement and Laplacian and Taubin smoothing. The book also discusses the optimization of cable nets and steel frames using nature-inspired metaheuristic methods.

Recent worldwide advances in manufacturing technologies led to a metamorphism in the industry. Fast-changing technologies on the product front have created a need for an equally fast response from manufacturing industries, who select manufacturing strategies, product designs, manufacturing processes, and machinery and equipment. Decision makers have the problem of assessing a range of options and selecting one based on conflicting criteria.

This book shows how graph theory and matrix approach, and fuzzy multiple attribute decision making methods can be used in manufacturing. Part I introduces the decision making situations in the manufacturing environment and presents decision making methods; Part II uses case studies to illustrate the applications of these methods in real manufacturing situations.

This book will interest designers, manufacturing engineers, practitioners, managers, institutes involved in design and manufacturing related projects, researchers, academics, and graduates in this field.

This book delves into various solution paradigms such as artificial neural network, support vector machine, wavelet transforms, evolutionary computing, swarm intelligence. During the last decade, novel solution technologies based on human and species intelligence have gained immense popularity due to their flexible and unconventional approach. New analytical tools are also being developed to handle big data processing and smart decision making. The idea behind compiling this work is to familiarize researchers, academicians, industry persons and students with various applications of intelligent techniques for producing sustainable, cost-effective and robust solutions of frequently encountered complex, real-world problems in engineering and science disciplines. The practical problems in smart grids, communication, waste management, elimination of harmful elements from nature, etc., are identified, and smart and optimal solutions are proposed. 

This book introduces readers to the “Jaya” algorithm, an advanced optimization technique that can be applied to many physical and engineering systems. It describes the algorithm, discusses its differences with other advanced optimization techniques, and examines the applications of versions of the algorithm in mechanical, thermal, manufacturing, electrical, computer, civil and structural engineering.

In real complex optimization problems, the number of parameters to be optimized can be very large and their influence on the goal function can be very complicated and nonlinear in character. Such problems cannot be solved using classical methods and advanced optimization methods need to be applied. The Jaya algorithm is an algorithm-specific parameter-less algorithm that builds on other advanced optimization techniques. The application of Jaya in several engineering disciplines is critically assessed and its success compared with other complex optimization techniquessuch as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and other recently developed algorithms.

This book comprises peer-reviewed papers presented at the International Conference on Advanced Engineering Optimization Through Intelligent Techniques (AEOTIT) 2022. The book combines contributions from academics and industry professionals and covers advanced optimization techniques across all major engineering disciplines like mechanical, manufacturing, civil, automobile, electrical, chemical, computer, and electronics engineering. The book discusses different optimization techniques and algorithms such as genetic algorithm, non-dominated sorting genetic algorithm-II, and III, differential search, particle swarm optimization, fruit fly algorithm, cuckoo search, teaching–learning-based optimization algorithm, grey wolf optimization, Jaya algorithm, Rao algorithms, and many other latest meta-heuristic techniques and their applications. Various multi-attribute decision-making methods such as AHP, TOPSIS, ELECTRE, PROMETHEE, DEMATEL, R-method, fuzzy logic, and their applications are also discussed. This book serves as a valuable reference for students, researchers, and practitioners and helps them in solving a wide range of optimization problems.

This book comprises peer-reviewed papers presented at the 4th International Conference on Advanced Engineering Optimization Through Intelligent Techniques (AEOTIT) 2023. The book combines contributions from academics and industry professionals and covers advanced optimization techniques across all major engineering disciplines like mechanical, manufacturing, civil,  electrical, chemical, computer, and electronics engineering. The book discusses different optimization techniques and algorithms such as genetic algorithm, non-dominated sorting genetic algorithm-II, and III,  particle swarm optimization, gravitational search algorithm, ant lion optimization, dragonfly algorithm, teaching–learning-based optimization algorithm, grey wolf optimization, Jaya algorithm, Rao algorithms, many other latest meta-heuristic techniques, machine learning algorithms, and their applications. Various multi-attribute decision-making methods such as AHP, TOPSIS,  PROMETHEE, desirability function,  SWARA, R-method, BHARAT method, Taguchi method, fuzzy logic, and their applications are also discussed. This book serves as a valuable reference for students, researchers, and practitioners and helps them in solving a wide range of optimization problems.