Plenary Lecture

Swarm Intelligence Algorithms Hybridization


Professor Milan Tuba
Megatrend University Belgrade
Faculty of Computer Science

Abstract: Most real-life problems in almost every field of science, business, engineering etc. can be modeled as some kind of optimization problem. Powerful mathematical techniques have been developed over centuries for most optimization problems, however a class of problems of great practical importance remains computationally intractable. When the problem dimension is high and when there are many local optima, the traditional deterministic methods cannot cope with the computational complexity of the problem and the use of nondeterministic optimization metaheuristics is more promising. Swarm intelligence is a relatively new branch of nature inspired algorithms that very successfully find suboptimal solutions to hard optimization problems in a reasonable amount of computational time, by simulating collective intelligence of swarms of very simple agents like bees, ants, fireflies etc. Swarm intelligence metaheuristics employ iterative, population based, stochastic approach, and do not make any assumptions about the fitness landscape. They are based on intensification (exploitation) and diversifications (exploration) where intensification performs search around the current best solutions, while diversification explores the search space more broadly by conducting essentially a random search. Swarm intelligence algorithms exhibit excellent performance on many hard optimization problems, however for many problems the results remain unsatisfying. Success depends on the balance between exploitation and exploration and better balance can often be achieved by hybridization i.e. using combination of appropriate elements from two or more different algorithms. Selection of hybridization elements has to be carefully targeted so that the advantages of one algorithm overcome shortcomings of the other. This plenary lecture presents some successful hybridizations of various recent swarm intelligence algorithms.

Brief Biography of the Speakers: Milan Tuba is the Dean of Graduate School of Computer Science and Provost for mathematical, natural and technical sciences at Megatrend University of Belgrade. He received B. S. in Mathematics, M. S. in Mathematics, M. S. in Computer Science, M. Ph. in Computer Science, Ph. D. in Computer Science from University of Belgrade and New York University. From 1983 to 1994 he was in the U.S.A. first as a graduate student and teaching and research assistant at Vanderbilt University in Nashville and Courant Institute of Mathematical Sciences, New York University and later as Assistant Professor of Electrical Engineering at Cooper Union School of Engineering, New York. During that time he was the founder and director of Microprocessor Lab and VLSI Lab, leader of scientific projects and theses supervisor. From 1994 he was Assistant Professor of Computer Science and Director of Computer Center at University of Belgrade, from 2001 Associate Professor, Faculty of Mathematics, University of Belgrade, and from 2004 also a Professor of Computer Science and Dean of the College of Computer Science, Megatrend University Belgrade. He was teaching more than 20 graduate and undergraduate courses, from VLSI Design and Computer Architecture to Computer Networks, Operating Systems, Image Processing, Calculus and Queuing Theory. His research interest includes mathematical, queuing theory and heuristic optimizations applied to computer networks, image processing and combinatorial problems. Dean Tuba is the author or coauthor of more than 150 scientific papers and coeditor or member of the editorial board or scientific committee of number of scientific journals and conferences. Member of the ACM, IEEE, AMS, SIAM, IFNA.