ABSTRACT: Scalability of optimization algorithms is a longstanding issue that every researcher and applicationist is worried about. An algorithm working extremely well in low-dimensional problems may not work at all for large-sized problems. In this talk, we present a population-based optimization algorithm that finds a near-optimal solution to an industry-sponsored real-world integer linear programming problems for a wide range of problem sizes, extending to a staggering billion variable problem. This is remarkable and probably stands as the first-ever optimization study on such a large-sized real-world problem, as standard commercial softwares cannot solve more than 1,000 variable version of the problem. Reasons for the success of the proposed approach will be discussed.
Bio-skecth of the speaker:
Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in optimization, modeling, and machine learning. He was awarded 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 and Humboldt Fellowship from Germany. He is fellow of IEEE, ASME, and three Indian science and engineering academies. He has published over 410 research papers with Google Scholar citation of 76,000 with h-index 91. He is in the editorial board on 20 major international journals. More information about his research contribution can be found from http://www.egr.msu.edu/~kdeb.