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The book thoroughly explains fundamental optimization concepts and terminology, including variables, parameters, constraints, bounds, and convexity. It also demonstrates how to formulate optimization problems using illustrative examples. Covering both single-variable and multi-variable optimization methods, the book provides theoretical insights, practical examples, and exercises, along with a graphical approach to problem-solving.
In light of growing concerns about resource limitations and environmental impacts, this textbook addresses the need for efficient resource use amidst technological advancements and market competition. Students will appreciate the comprehensive coverage, supported by illustrations and exercises that deepen their understanding. Instructors will find it invaluable for classroom teaching, with accessible concepts and practical examples that highlight the nuances of optimization.
Inhaltsverzeichnis
Chapter 1 Introduction to Optimization.- Chapter 2 Single Variable Optimization Methods.- Chapter 3 Multi Variable Optimization Methods.- Chapter 4 Graphical Optimization.- Chapter 5 Linear Programming Methods.- Chapter 6 Nature inspired Optimization Methods.
Über den Autor / die Autorin
Anand J. Kulkarni holds a PhD in Artificial Intelligence (AI) based Distributed Optimization from Nanyang Technological University, Singapore, an MS in AI from the University of Regina, Canada, a Bachelor of Mechanical Engineering from Shivaji University, India, and a Diploma from the Board of Technical Education, Mumbai. He worked as a Postdoctoral Research Fellow at the Odette School of Business, University of Windsor, Canada, and spent over six years at Symbiosis International University, Pune, India. Dr. Kulkarni is a Research Professor and Associate Director of the Institute of Artificial Intelligence at MITWPU, Pune, India. His research interests include AI-based nature-inspired optimization algorithms and self-organizing systems. Anand has pioneered several optimization methodologies, including Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, and Socio-Evolution & Learning Optimization Algorithm.
As the founder of OAT Research Lab, Anand has published over 70 research papers in peer-reviewed journals, book chapters, and conference proceedings, along with authoring 6 books and editing 12 others. He serves as the lead series editor for the journals and book series of reputed publishers. In addition to his academic contributions, Anand writes on AI topics for various newspapers and magazines and has delivered expert research talks in countries including the USA, Canada, Singapore, Malaysia, India, Australia, Dubai, and France.
Zusammenfassung
The book thoroughly explains fundamental optimization concepts and terminology, including variables, parameters, constraints, bounds, and convexity. It also demonstrates how to formulate optimization problems using illustrative examples. Covering both single-variable and multi-variable optimization methods, the book provides theoretical insights, practical examples, and exercises, along with a graphical approach to problem-solving.
In light of growing concerns about resource limitations and environmental impacts, this textbook addresses the need for efficient resource use amidst technological advancements and market competition. Students will appreciate the comprehensive coverage, supported by illustrations and exercises that deepen their understanding. Instructors will find it invaluable for classroom teaching, with accessible concepts and practical examples that highlight the nuances of optimization.
