Prof. Yaroslav D. Sergeyev

Distinguished Professor, Head of Numerical Calculus Laboratory, University of Calabria, Italy

Biography: Yaroslav D. Sergeyev, Ph.D., D.Sc., D.H.C. is Distinguished Professor at the University of Calabria, Italy (chiamata diretta per “chiara fama”) and Head of Numerical Calculus Laboratory at the same university. His research interests include numerical analysis, global optimization (in 2017-2021 he was President of the International Society of Global Optimization), infinity computing and calculus, philosophy of computations, set theory, number theory, fractals, parallel computing, and interval analysis. Prof. Sergeyev was awarded several research prizes (Khwarizmi International Award, 2017; Pythagoras International Prize in Mathematics, Italy, 2010; EUROPT Fellow, 2016; Outstanding Achievement Award from the 2015 World Congress in Computer Science, Computer Engineering, and Applied Computing, USA; Honorary Fellowship, the highest distinction of the European Society of Computational Methods in Sciences, Engineering and Technology, 2015; The 2015 Journal of Global Optimization (Springer) Best Paper Award; Lagrange Lecture, Turin University, Italy, 2010; MAIK Prize for the best scientific monograph published in Russian, Moscow, 2008, etc.).  His list of publications contains more than 290 items (among them 6 books). He is a member of editorial boards of 12 international journals and co-editor of 11 special issues. He delivered more than 70 plenary and keynote lectures at prestigious international congresses. He was (Co-)Chairman of 11 international conferences and a member of Scientific Committees of more than 70 international congresses.

The Infinity Computer for Single- and Multi-Objective Optimization

Abstract: In this talk, a recent computational methodology is described. It has been introduced with the intention to allow one to work with infinities and infinitesimals numerically in a unique computational framework. It is based on the principle ‘The part is less than the whole’ applied to all quantities (finite, infinite, and infinitesimal) and to all sets and processes (finite and infinite). The methodology uses as a computational device the Infinity Computer (a new kind of supercomputer patented in USA and EU) working numerically with infinite and infinitesimal numbers that can be written in a positional system with an infinite radix. On a number of examples (numerical differentiation, divergent series, ordinary differential equations, fractals, set theory, etc.) it is shown that the new approach can be useful from both theoretical and computational points of view. The main attention is dedicated to applications in optimization (local, global, and multi-objective). The accuracy of the obtained results is continuously compared with results obtained by traditional tools used to work with mathematical objects involving infinity. The Infinity Calculator working with infinities and infinitesimals numerically is shown during the lecture. For more information see the dedicated web page http://www.theinfinitycomputer.com and this survey: Sergeyev Ya.D. Numerical infinities and infinitesimals: Methodology, applications, and repercussions on two Hilbert problems, EMS Surveys in Mathematical Sciences, 2017, 4(2), 219–320. The web page developed at the University of East Anglia, UK is dedicated to teaching the methodology: https://www.numericalinfinities.com/

Prof. Andries Engelbrecht

University of Stellenbosch, South Africa

Biography: Prof 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”.

Set-based Particle Swarm Optimization and Its Applications

Abstract: Particle swarm optimization (PSO) was originally developed to solve single-objective, static, continuous-valued optimization problems under the presence of box constraints. While most PSO algorithms were developed to solve continuous-valued optimization problems, PSO variations have been developed to solve binary-valued, integer-valued, and general discrete-valued optimization problems. A caveat of these approaches is that all particle position and velocity vectors must be of the same length -- variable length solutions are not allowed in these PSO variations. Set-based PSO algorithms have been developed to allow variable sized solutions to discrete-valued optimization problems that can be formulated as set-based optimization problems. This talk will present a generic formulation of a set-based PSO, where all the arithmetic operations of the velocity and particle position update equations are formulated in terms of set operators. The generic application of this set-based PSO will then be illustrated on a wide range of single-objective optimization problems, including the multi-dimensional knapsack problem, feature selection, RNA secondary structure prediction, polynomial approximation, data clustering, training of support vector machines, rule extraction, and portfolio optimization. The talk will conclude with extensions of the set-based PSO to multi-objective set-based optimization problems, including the multi-objective multi-dimensional knapsack problem and multi-objective portfolio optimization.　

Prof. Shengxiang Yang

De Montfort University, UK

Biography: Shengxiang Yang (http://www.tech.dmu.ac.uk/~syang/) got his PhD degree in Control Theory and Control Engineering from Northeastern University, China in 1999. He is now a Professor of Computational Intelligence (CI) and Deputy Director of the Institute of Artificial Intelligence (IAI), School of Computer Science and Informatics, De Montfort University, UK. He has worked extensively for many years in the areas of CI methods, including evolutionary computation, artificial neural networks, data mining and data stream analysis, and their applications for real-world problems. He has over 360 publications with an H-index of 64 according to Google Scholar. His work has been supported by UK research councils, EU FP7 and Horizon 2020, and industry partners. He serves as an Associate Editor or Editorial Board Member of several international journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences, and Enterprise Information Systems, etc. He was the founding chair of the Task Force on Intelligent Network Systems (TF-INS, 2012-2017) and the chair of the Task Force on EC in Dynamic and Uncertain Environments (ECiDUEs, 2011-2017) of the IEEE Computational Intelligence Society (CIS). He has given around 30 invited keynote speeches and tutorials at international conferences.

Swarm Intelligence in Dynamic Environments

Abstract: Swarm intelligence (SI) in biology represents the property that the collective behavior of a swarm of agents that interact locally with their environment causes coherent functional global patterns to emerge. SI algorithms are optimization algorithms inspired from the SI phenomena in biology, such as ant foraging and bird flocking, and have been applied in different fields. Most SI algorithms have been developed to address stationary problems. However, many real-world problems are dynamic optimization problems (DOPs) that are subject to changes over time. DOPs have attracted a growing interest from the SI community in recent years due to the importance in the real-world applications of SI algorithms. This talk will first briefly introduce the concepts of SI and DOPs, review the enhancement strategies integrated into SI algorithms to address DOPs, and then describe several detailed case studies on SI methods for DOPs. Finally, some conclusions will be made and the future work on SI for DOPs will be briefly discussed.