Machine Learning Assisted Evolutionary Multi And Many Objective Optimization
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Author: Dhish Kumar Saxena
language: en
Publisher: Springer Nature
Release Date: 2024-05-17
This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO). EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMâOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMâO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners. To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types. Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMâO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMâOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMâOA domain. To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMâOA and ML domains.
Innovization
Every designer wants to know what makes a product or process optimal. This book suggests a holistic approach to optimization that involves two steps: find a set of trade-off optimal solutions involving two or more conflicting objectives related to the problem, and then analyze these high-performing solutions to determine solution principles that commonly prevail among these solutions. Since the solutions are optimal, such common principles are likely to exist; and since these principles are common to many solutions they are likely to provide robust, reliable solution principles. The author is one of the leading researchers in multiobjective optimization, and an expert in design methodology. In this book he offers introductions to innovation in design; multiobjective optimization, in particular evolutionary multiobjective optimization (EMO) techniques that find multiple, trade-off, optimal solutions; and knowledge extraction from multivariate data using graphical, regression and clustering techniques. He then introduces his innovization methodology for revealing new, innovative design principles related to decision variables and objectives, and he demonstrates it through engineering case studies, in particular product and process design problems. The book will be of benefit to practitioners, researchers and students engaged with issues of optimal design, in particular in domains such as engineering design, product design, engineering optimization, manufacturing, process design and complex systems. The sample computer code referenced is available from the author's website.
Data-Driven Evolutionary Optimization
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.