Optimization For Engineering Design Kalyanmoy Deb Pdf

[Ilona Brownson]

Toaid 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 EMOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMOA and ML domains.

Kalyanmoy Deb is University Distinguished Professor and Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. His research interests are in evolutionary optimization and their application inmulti-criterion optimization, modeling, and machine learning. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in EMO, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He is fellow of IEEE and ASME.

Erik D. Goodman was PI and Director of BEACON Center for the Study of Evolution in Action, an NSF Center headquartered at Michigan State University, 2010-2018. He was Professor of Electrical & Computer Engineering, also Mechanical Engineering and Computer Science & Engineering, until retiring in 2022. He co-founded Red Cedar Technology (1999, now part of Siemens), and developed the HEEDS SHERPA commercial design optimization software. Honors include Michigan Distinguished Professor of the Year, 2009; MSU Distinguished Faculty Award, 2011; Senior Fellow, International Society for Genetic and Evolutionary Computation, 2004; Founding Chair, ACM SIG on Genetic and Evolutionary Computation (SIGEVO), 2005-2007.

In this paper, we apply an elitist multi-objective genetic algorithm for solving mechanical component design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods in that they can find multiple Pareto-optimal solutions in one single simulation run. Recently, we proposed a much improved version of the originally proposed non-dominated sorting GA (we call NSGA-II) in that it is computationally faster, uses an elitist strategy, and it does not require fixing any niching parameter. In this paper, we use NSGA-II to handle constraints by using two implementations. On four mechanical component design problems borrowed from the literature, we show that the NSGA-II can find a much wider spread of solutions than classical methods and the NSGA. The results are encouraging and suggests immediate application of the proposed method to other more complex engineering design problems.

The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

> Catalog 2021 > Master's degree > Master in Electronics, electrical energy, automation > Electrical Engineering and Control Systems / CompSEE 2nd year > UE Optimization Methods for Components and Systems

Optimal design aims at finding solution of complex design problems using optimization algorithms. The objectives of this course is to presents the fundamentals of an engineering design study, problem specification, modelling for design, optimization algorithms.

The Infosys Prize for Engineering and Computer Science is awarded to Professor Kalyanmoy Deb for his contributions to the emerging field of Evolutionary Multi-objective Optimization (EMO) that has led to advances in non-linear constraints, decision uncertainty, programming and numerical methods, computational efficiency of large-scale problems and optimization algorithms.

In 2003, Deb made a major contribution by suggesting the concept of innovization - a procedure to discover innovative solution principles through multi-criteria optimization. Since the pareto-optimal solutions are all optimal corresponding to different trade-offs among search objectives, Deb argued and demonstrated that these solutions must have some common principles that qualify them to be optimal. He then suggested a systematic data-mining procedure to unveil such valuable principles in many real-world design and other problem-solving tasks. The innovization procedure utilizes mathematical optimality conditions in its core and reveals valuable problem information such as, What problem properties make a solution optimal? Answers to such questions enabled designers to comprehend a deeper understanding of a problem than before.

A multi-criteria problem-solving task involves optimization and decision-making procedures together. Deb made lead attempts in suggesting combined algorithms that incorporate a decision-maker's (DM) preference information while the optimization is underway. This basically led to computationally efficient and interactive procedures, which, instead of finding the entire Pareto-optimal front, resulted in focusing on the DM's preferred solution regime quickly and accurately. Deb and others extended this concept by borrowing multi-criteria decision-making concepts to build efficient decision support systems for handling practical problems.

Deb has made several salient contributions in other areas in optimization as well. A number of his proposed algorithms have become standard practice within the evolutionary optimization community. His parameter-less constraint handling strategy suggested in 2000 is so far the simplest and most popular method for handling non-linear constraints. His optimization algorithm for handling real-valued parameters using probability-based search operators is routinely employed and has been adopted in commercial optimization software such as iSight, ModeFRONTIER, and VisualDoc.

Deb advocated the use of customized optimization procedures for real-world problem solving and demonstrated their need in solving large-scale mixed-integer programming problems which were difficult to solve by classical optimization methods. His development of a scalable customized genetic algorithm, for the first time, found a near-optimal solution of a million-variable integer linear-programming problem in a polynomial time complexity, marking this as one of the landmark applications of evolutionary optimization. Such problems could only be solved earlier up to a few thousand variables using classical means.

Prof. Kalyanmoy Deb received his Bachelor's degree in Mechanical Engineering from IIT Kharagpur in 1985, and his PhD in Engineering from the University of Alabama. He was a Visiting Research Assistant Professor in the Department of General Engineering at the University of Illinois, Urbana Champaign between 1991 and 1992 and worked at the Illinois Genetic Algorithms Laboratory (IlliGAL). He later joined IIT Kanpur and established the Kanpur Genetic Algorithms Laboratory (KanGAL) there in 1997 where he was a professor in the Mechanical Engineering department. In 2013, he moved to the Michigan State University where he is now a Professor at three departments and also an endowed chair professor of the ECE department.

Prof. Deb holds an Adjunct Professor position at the Department of Information and Service Economy at Aalto University School of Economics, Helsinki, Finland. In addition, he is a Velux Foundation Guest Professor at the Technical University of Denmark.

He is the author of more than 280 research papers, two textbooks and 17 edited books. His 2001 book, Multi-Objective Optimization Using Evolutionary Algorithms, is the first ever compilation of multi-objective optimization algorithms.

Prof. Deb is the recipient of numerous awards, including the Outstanding Graduate Research Assistant award for two consecutive years at Alabama, the Shanti Swarup Bhatnagar Prize in Engineering Sciences in 2005, the Friedrich Wilhelm Bessel Research Award and Humboldt Fellowship from Alexander von Humboldt Foundation, Germany, the Distinguished Alumnus Award from his Alma mater IIT Kharagpur in 2011, Cajastur Mamdani Prize for Soft Computing from European Center for Soft Computing in 2011, and the Thomson Citation Laureate Award, an award given to an Indian Researcher for making the most highly cited research contribution during 1996-2005 in a particular discipline according to the ISI Web of Science.

He is a fellow of the Indian National Science Academy (INSA) , Indian National Academy of Engineering (INAE) , Indian Academy of Sciences (IASc) , and International Society of Genetic and Evolutionary Computation (ISGEC). Professor Deb is also on 18 international journal editorial boards including IEEE Transactions on Evolutionary Computation, Evolutionary Computation (MIT Press) , Engineering Optimization , Genetic Programming and Evolvable Machines and others. He is the only Asian Executive Council Member of the International Society for Genetic and Evolutionary Computation (ISGEC).

Professor Kalyanmoy Deb has made fundamental contributions to the emerging field of Evolutionary Multi-objective Optimization (EMO) where his work has led to significant advances in the areas of non-linear constraints, decision uncertainty, programming and numerical methods, computational efficiency of large-scale problems and optimization algorithms. He has demonstrated how fundamental ideas of optimization and computing principles can be combined to devise efficient algorithms that are fast, accurate and scalable. His recent studies on handling challenging practical multi-criteria optimization problems make his research pragmatic and applicable to multiple disciplines. Deb's research addresses both fundamental and applied aspects of optimization, developed synergistic and computationally efficient algorithms, and demonstrates their usefulness in industries such as logistics and refineries.

N2 - Domain experts can benefit from optimisation simply by getting better solutions, or by obtaining knowledge about possible trade-offs from a Pareto front. However, just providing a better solution based on objective function values is often not sufficient. It is desirable for domain experts to understand design principles that lead to a better solution concerning different objectives. Such insights will help the domain expert to gain confidence in a solution provided by the optimiser. In this paper, the aim is to learn heuristic rules on building spatial design by data-mining multi-objective optimisation results. From the optimisation data a domain expert can gain new insights that can help engineers in the future; this is termed innovization. Originally used for applications in mechanical engineering, innovization is here applied for the first time for optimisation of building spatial designs with respect to thermal and structural performance.

3a8082e126