Keynote Speakers
Prof. Celso C. Ribeiro
Universidade Federal Fluminense, Brazil
Celso
C. Ribeiro has a bachelor’s degree in electrical engineering (1976)
and a masters in systems engineering (1978). His main research areas
include Combinatorial Optimization, Algorithm Design,
Metaheuristics, and Sports Scheduling. He obtained a doctorate in
Computer Science at École Nationale Supérieure des
Télécommunications (France, 1983) and the Habilitation (HDR) at
Université Paris 13 (France, 1990). Celso Ribeiro chaired the
Departments of Electrical Engineering (1983-1987) and Computer
Science (1993-1995) of the Catholic University of Rio de Janeiro. He
coordinated the graduate program in Computer Science of Universidade
Federal Fluminense (2009-2017), Brazil, where he is a Full
Professor. Dr. Ribeiro also chaired the Department of Modernization
Programs of the Brazilian Ministry of Education (2005-2007) and
acted as Subsecretary of Education of the State of Rio de Janeiro
(2007-2008), Brazil. He was President of the Brazilian Operations
Research Society (SOBRAPO, 1989-1990), President of the
Latin-American Association of Operations Research Societies (ALIO,
1992-1994), and Regional Vice-President of the International
Federation of Operational Research Societies (IFORS, 1998-2000). He
co-authored the books "Combinatorial Models for Scheduling Sports
Tournaments" (2023) and "Optimization by GRASP: Greedy Randomized
Adaptive Search Procedures" (2016), both published by Springer. He
is the editor of four books and the author of more than 160 papers
in international journals and 22 book chapters. He holds two US
patents and has supervised 32 doctorate dissertations and 39 master
theses. He is the General Editor of the journal International
Transactions in Operational Research (since 2007) and a member of
the editorial board of several journals such as Engineering
Applications of Artificial Intelligence, Discrete Optimization,
Journal of Heuristics, and RAIRO Operations Research. He was also
the Editor-in-Chief of the Journal of the Brazilian Computing
Society (2014-2015). Dr. Ribeiro is a member of the Brazilian
Academy of Sciences. He was awarded the title of Doctor Honoris
Causa by Universidad Nacional de San Agustin de Arequipa (Peru,
2010) and the Medal of the National Order of Scientific Merit
(Brazil, 2018).
Prof. Andries Engelbrecht
Stellenbosch University, South Africa
Speech Title: Multi-Guide Particle Swarm Optimization: An
Efficient Approach to Multi- and Many-Objective Optimization
Abstract: A number of particle swarm optimization (PSO)
algorithms have been developed to solve multi-objective
optimization problems (MOPs). However, most of these
multi-objective PSO (MOPSO) algorithms are computationally
complex, have a number of problem dependent control parameters,
and do not scale well to many-objective optimization problems
(MaOPs) and large-scale MOPs. This talk will present a recent
MOPSO algorithm, the multi-guide PSO which makes use of
sub-swarms to solve MOPs. Each sub-swarm optimizes one objective
function independent of the other objectives, but exchanges
information about all objectives via an archive guide selected
from the set of non-dominated solutions. The result is a
computationally efficient MOPSO algorithm, which has been shown
to outperform state-of-the-art MOPSO algorithms and
multi-objective evolutionary algorithms. In addition, the
sub-swarm approach facilitates scalability to many-objectives
without having to change the core algorithm to cope withe the
consequences of having to optimize many objectives. Again,
results will show that the MGPSO performs better than algorithms
purposefully designed to solve MaOPS. While the MGPSO does
introduce additional control parameters, the algorithm is the
only MOPSO with formal theoretical analysis of conditions on the
control parameters that will guarantee that an equilibrium state
will be reached. These conditions have been used to develop a
stability guided MGPSO that does not require time consuming
tuning of its control parameters. Results will be presented to
showcase the value of the MGPSO for solving MOPs and MaOPs.
Biography: Andries Engelbrecht received the Masters and PhD
degrees in Computer Science from the University of Stellenbosch,
South Africa, in 1994 and 1999 respectively. He is currently
appointed as the Voigt Chair in Data Science in the Department
of Industrial Engineering, with a joint appointment as Professor
in the Computer Science Division, Stellenbosch University. Prior
to 2019, he was appointed appointed in the Department of
Computer Science, University of Pretoria (1998-2018), where he
served as the head of the department (2008–2017), South African
Research Chair in Artificial Intelligence (2007–2018), and
Director of the Institute for Big Data and Data Science
(2017–2018). His research interests include swarm intelligence,
evolutionary computation, artificial neural networks, artificial
immune systems, machine learning, data analytics, and the
application of these Artificial Intelligence paradigms to data
mining, games, bioinformatics, finance, and difficult
optimization problems. He is author of two books, “Computational
Intelligence: An Introduction” and “Fundamentals of
Computational Swarm Intelligence”.
Dr. Abhishek Gupta
Agency for Science, Technology and Research (A*STAR), Singapore
Speech
Title: Transfer optimization of expensive objective functions
Abstract: Advances at the intersection of optimization and
machine learning promise new avenues for the efficient discovery of
diverse yet high-quality engineering solutions. However, unlike many
commercial applications where data is abundant, engineering problems
are often characterized by data scarcity – requiring expensive
numerical simulations and/or complex real-world procedures to
acquire data. This attribute reduces the impact potential of modern
generative algorithms that are known to be data hungry. As an
alternative, transfer optimization promises a novel approach to the
sample-efficient discovery of near-optimal solutions to a target
optimization task by utilizing experiential priors from a relatively
small number of related source tasks. In this talk, I shall present
some recent algorithmic developments in the area of transfer
optimization, applicable to both single- and multi-objective
optimization problems. The algorithms under consideration will
leverage ideas from probabilistic model-based search, with
particular emphasis on evolutionary computation and
surrogate-assisted optimization. Examples of the practical utility
of these algorithms shall be showcased in domains including
manufacturing and complex design.
Biography: Abhishek Gupta
received the Ph.D. in Engineering Science from the University of
Auckland, New Zealand, in 2014. He is a Scientist at the Agency for
Science, Technology and Research (A*STAR) Singapore and has held
prior appointments as Research Scientist at the Nanyang
Technological University, Singapore. Abhishek has diverse research
experience in the fields of computational science and computational
intelligence, with recent research interest at the intersection of
optimization, neuroevolutionary algorithms, probabilistic
model-based methods and scientific machine learning. He received the
2019 and the 2023 IEEE Transactions on Evolutionary Computation
Outstanding Paper Awards for pioneering work on the theory and
algorithms of multitask evolutionary computation. He was also
recognized as the 2021 IEEE Transactions on Emerging Topics of
Computational Intelligence Outstanding Associate Editor. Abhishek is
currently also an Associate Editor of the IEEE Transactions on
Evolutionary Computation, and on the Editorial Boards of Complex &
Intelligent Systems, Memetic Computing, and the Springer Book Series
on Adaptation, Learning, and Optimization. He is an IEEE Senior
Member and founding chair of the IEEE CIS Task Force on Multitask
Learning and Multitask Optimization.