Plenary Lecture

Fireworks Algorithm Capabilities and Applications

Professor Milan Tuba
John Naisbitt University
Faculty of Computer Science
Belgrade, Serbia

Abstract: Fireworks algorithm is one of the recent population based stochastic optimization metaheuristics. It has been introduced by Tan and Zhu in 2010 and tested on standard unconstrained optimization benchmarks where it proved its potential. Exploitation (intensification) and exploration (diversification) capabilities of the proposed algorithm are based on the simulation of a well manufactured firework with numerous sparks positioned in the vicinity of the explosion and badly manufactured firework with few sparks scattered far from the point of explosion. Most of the real world problems can be modelled as some kind of optimization problem and such optimization problems often belong to the class of hard optimization problems for which deterministic mathematical methods cannot find solutions. On the other hand, population based stochastic metaheuristics, especially swarm intelligence, are widely and successfully used for such hard optimization problems and they represent an important and active research field. Fireworks algorithm was successfully used for a number of practical problems. However, some deficiencies were noticed, including inefficiently when objective functions were shifted from the origin and high computational cost. The author of the original fireworks algorithm (Zheng, Janecek and Tan) introduced in 2013 a new version called enhanced fireworks algorithm that addressed and rectified the mentioned deficiencies. The new version included five modifications, namely a new minimal explosion amplitude check, a new operator for generating explosion sparks and also a new operator for generating Gaussian sparks, a new mapping strategy for sparks which were out of the search space and the new operator for selecting the population for the next generation. This enhanced fireworks version of the algorithm is used today and it is one of the most successful swarm intelligence algorithms. In this plenary lecture some recent successful applications of the enhanced fireworks algorithm by the speaker will be presented, including coverage and node localization problems in wireless sensor and ad hoc networks, RFID planning problem, support vector machine parameters optimization, constrained portfolio optimization and multilevel image thresholding.

Brief Biography of the Speaker: Milan Tuba is the Dean of Graduate School of Computer Science and Provost for mathematical, natural and technical sciences at John Naisbitt 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. Prof. 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.

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