Engineering Optimization By Kalyanmoy Deb Pdf

[Merja Caryk]

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KD: Thank you, Dave, for your wishes and giving me this unique opportunity. I would also like to take this opportunity to acknowledge and appreciate your many years of leadership and active research and promoting the evolutionary computation field.

DF: Thanks very much. I remember when we were each starting out in evolutionary algorithms back in the 1980s that even getting 1 citation was pretty nice. One hundred thousand is quite an achievement.

DF: Before we move to the present day, what are your recollections of doing evolutionary algorithms at first with computers that were at the speed of, say, a Mac II? (For those who remember what a Mac II is.)

KD: Most of the optimization literature is built up on the idea of single-objective optimization, in which the sole purpose is to minimize or maximize a single goal or a single objective function. This is great if an application demands a sole focus of minimizing weight or cost, or maximizing a single performance metric. When such an optimization task is performed, it usually results in a very specialized solution that is best for the chosen objective. However, the solution is almost useless for other equally important goals or objectives. Most pragmatic engineering problem-solving tasks demand simultaneous optimization of more than one conflicting objectives, such as minimization of weight and simultaneous maximization of a performance metric.

KD: While we had a rough idea of our proposed approach, four of us met on a Saturday morning in my computer lab. While I narrated a version of idea to Sameer to code and produce some results, I moved to the next seat to discuss a slightly different idea with Amrit. Then, I discussed another related idea to Meyrivan. The students were so quick and apt with their coding skills that by the time I finished with Meyrivan, Sameer wanted to me check on some results on test problems that he was getting. Parallel processing was very effective and their skill and zeal enabled us to come up with the NSGA-II procedure by noon!

DF: When moving from a Pareto set of solutions to a particular engineering implement, we often have to choose a particular single solution. What rationale do you utilize for helping select a solution, presumably from the Pareto front?

KD: This is an area EMO researchers have not paid much attention to, yet. Every time I hear an EMO application presentation, I am always interested in this aspect, but in almost all cases the presentation ends with a visual representation of obtained trade-off solutions and there is no mention of how a single preferred solution may be chosen for the specific problem.

Rituparna Datta is a postdoctoral research fellow with the Robot Intelligence Technology (RIT) Laboratory at the Korea Advanced Institute of Science and Technology (KAIST). He earned his PhD in Mechanical Engineering at Indian Institute of Technology (IIT) Kanpur and thereafter worked as a Project Scientist in the Smart Materials, Structures, and Systems Lab at IIT Kanpur. His current research work involves investigation of Evolutionary Algorithms-based approaches to constrained optimization, applying multi-objective optimization in engineering design problems, memetic algorithms, derivative-free optimization, and robotics. He is a member of ACM, IEEE, and IEEE Computational Intelligence Society. He has been invited to deliver lectures in several institutes and universities across the globe, including at the Trinity College Dublin (TCD), Delft University of Technology (TUDELFT), University of Western Australia (UWA), University of Minho, Portugal, University of Nova de Lisboa, Portugal, University of Coimbra, Portugal, and IIT Kanpur, India. He is a regular reviewer of IEEE Transactions on Evolutionary Computation, Journal of Applied Soft Computing, Journal of Engineering Optimization, Journal of The Franklin Institute, and International Journal of Computer Systems in Science and Engineering, and was in the program committee of Genetic and Evolutionary Computation Conference (GECCO 2014), iNaCoMM2013, GECCO 2013, GECCO 2012, GECCO 2011, eighth international conference on Simulated Evolution And Learning (SEAL 2010), international conference on molecules to materials (ICMM-06), and some Indian conferences. He has also chaired session in ACODS 2014 and UKIERI Workshop on Structural Health Monitoring 2012, GECCO 2011, IICAI 2011 to name a few. He was awarded an international travel grant (Young Scientist), from Department of Science and Technology, Govt. of India, in July 2011 and June 2012 and travel grants from Queensland University, Australia, June 2012, GECCO Student Travel Grant, ACM, New York.

Michigan State University Professor Kalyanmoy Deb was presented the 2018 IEEE CIS Evolutionary Computation Pioneer Award at the IEEE World Congress on Computational Intelligence (WCCI 2018) in Rio de Janeiro, Brazil. He was honored July 11 for his "pioneering contribution, development, and leadership in evolutionary multi-criterion optimization."

"Among Professor Deb's pioneering contributions to the field of evolutionary multi-objective optimization was the algorithm NSGA-II, which has been more widely used than any other evolutionary multi-objective optimization tool," Goodman explained. "He has led the community in development of this new field that has spurred both widespread academic research and worldwide industrial application."

Multi-criterion optimization has been applied to many aspects of science, engineering, economics, and logistics. It helps decision-makers optimize conflicting objectives, like minimizing costs while maximizing comfort or maximizing performance while minimizing resource consumption and emissions. In practical uses, it can help communities balance productivity, profitability, and environmental damage while serving as a guide for real-world decision making.

He received his bachelor's of science degree in mechanical engineering from IIT Kharagpur in 1985 and his master's and PhD degrees in engineering mechanics from the University of Alabama in 1989 and 1991.

In 2017, he received the inaugural Lifetime Achievement Award from Clarivate Analytics for his highly cited research contributions during ceremonies in New Delhi, India. He was also honored with the Clarivate Analytics Highly Cited Researcher Award in India. He was selected for papers ranking in the top one percent by citations, considering both year and field of publication.

Also at WCCI 2018, a paper co-authored by Deb and two former students -- Ankur Sinha, of the Indian Institute of Management Ahmedabad, India, and Samish Bedi, who visited MSU from India in 2016 as an inGEAR student -- received the IEEE Congress on Evolutionary Computation 2018 Outstanding Conference Paper Award.

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Kalyanmoy Deb is University Distinguished Professor and Koenig Endowed Chair Professor of Department of Electrical and Computer Engineering at Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has been working in evolutionary computation and optimization fields for the past 35 years. He has been a visiting professor at various universities across the world including University of Skvde in Sweden, Aalto University in Finland, Nanyang Technological University in Singapore, and IITs in India. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in evolutionary multi-criterion optimization (EMO), Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He received honorary Doctorate degree from University of Jyvaskyla, Finland in 2013. He is fellow of ACM, IEEE, ASME, and three Indian science and engineering academies. He has published over 620 research papers with Google Scholar citation of over 190,000 with h-index 135. He is in the editorial board on 10 major international journals. More information about his research contribution can be found from -lab.org.

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