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.

Covers advanced methodologies of optimization for product and system design Discusses real world in nature covering a wide variety of optimization methods Includes chapters from experts in this field that have proven background and elite publication record

Autorentext

Anand J Kulkarni holds a Ph.D. in Distributed Optimization from Nanyang Technological University, Singapore, an MS in Artificial Intelligence from the University of Regina, Canada, a Bachelor of Engineering from Shivaji University, India and a Diploma from the Board of Technical Education, Mumbai. He worked as a Research Fellow at Odette School of Business, University of Windsor, Canada. Anand worked with Symbiosis International University, Pune, India for over six years. He is currently working as a Professor and Associate Director of the Institute of Artificial Intelligence at MITWPU, Pune, India. His research interests include optimization algorithms, multi-objective optimization, continuous, discrete, and combinatorial optimization, swarm optimization, and self-organizing systems. Anand pioneered optimization methodologies such as Cohort Intelligence, Ideology Algorithm, Expectation Algorithm, and Socio Evolution & Learning Optimization Algorithm. Anand is the founder of Optimization and Agent Technology Research Lab and has published over 70 research papers in peer-reviewed reputed journals, chapters, and conferences along with 5 authored and 10 edited books. Anand is the lead series editor for Springer and Taylor & Francis as well as the editor of several Elsevier journals. He also writes on AI in several newspapers and magazines. Anand has delivered expert research talks in countries such as the USA, Canada, Singapore, Malaysia, India, and France.

Inhalt
Multi-objective Optimization of Ventilated Brake Disc based on Finite Element Simulation.- Multi Response Optimization on Process Parameters of WEDM for Ti-6Al-4V Alloy Using Grey Relational Approach.- Tuning of Complex Coefficient Fractional Complex Order Controllers for a Generalized System Structure - An Optimisation Approach.- A Review on Intelligent Optimization Techniques based Fault Detection and Diagnosis in Power System Applications.- Prediction of Surface Roughness using Desirability Concept and Support Vector Machine for Fused Deposition Modeling Part.- An Extremum Model for the Performance Analysis of a Loop Heat Pipe using Nano-fluids.- Selected Multi-Criteria Decision-Making Methods and their Applications to Product and System Design.- Cohort Intelligence Solution to Bank Asset Liability Management.- Cohort Intelligence Solution to Goal Programming Problems from Financial Management Domain.- Solving Asset and Liability Management Problem using Cohort Intelligence and GoalProgramming.- Proposing a New Feature Clustering Method in order to the Binary Classification of Covid-19 in Computed Tomography Images.- Deep Learning Framework for Brain Tumor and Alzheimer Disease Prognosis using MRI Images.- Genetic Algorithm to Maximize the Tourist's Satisfaction: An Assessment of Technology Adoption for a Tourist App.