VSB-Technical University of Ostrava, Czech Republic
Tutorial 1: Recent trends in parallel metaheuristics
Abstract: Manycore processors, Graphic Processing Units (GPUs), floating-point accelerators, and cloud environments represent an exciting new family of platforms for a truly parallel and distributed implementation and execution of Nature and Bio-inspired Algorithms. Manycore processors offer an unparalleled flexibility and power while the GPUs are a massively parallel single-instruction multiple-data (SIMD) devices that can nowadays reach peak performance of hundreds and thousands of giga FLOPS (floating-point operations per second). Nature-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for an optimal solution or a set of solutions of a given problem. Genetic Algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential Evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle Swarm Optimization (PSO) can be seen as a nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This lecture will review recent trends and advances in parallel and distributed metaheuristics powered by state-of-the-art parallel and distributed technology.
Tutorial 2: Bio-inspired Methods for Wireless Sensor Networks – Overview, Applications, and Examples
Abstract: Wireless sensor networks have recently emerged as a new class of distributed, multi-agent networks for large scale monitoring with high spatio-temporal resolution. These networks are faced with many requirements and challenges that include autonomous operation, strict energy constraints, low computing power, multi-hop communication, robustness, reliability, adaptability, and the ability to operate under harsh environmental conditions. Activity scheduling and routing are two complementary hard optimization problems that need to be tackled in order to optimize the operations of Wireless Sensor Networks. Bio-inspired methods including Ant Colony Optimization, Particle Swarm Optimization, Evolutionary Computation, and many others, have been in the last decade identified as a powerful class of algorithms that can be used to model and solve this type of problems. This lecture provides an overview of the most used applications of bio-inspired methods to the area of wireless sensor networks as well as several examples of recent algorithms and their applications. It will illustrate the validity and contribution of bio-inspired methods in the area of wireless sensor networking.
Biography: Pavel Krömer is an associate professor of Computer Science. He obtained M.Sc. and Ph.D. in Computer Science from the VSB – Technical University of Ostrava in 2006 and 2010, respectively. Between 2005 and 2010, he was employed as a software specialist by a large software company. In 2010, he returned to academia as an assistant professor and junior researcher at the Department of Computer Science, VSB – TU Ostrava, Czech Republic. During 2014, he served as a postdoctoral fellow at the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. Upon returning to the VSB – TU Ostrava, he gained the rank of associate professor and senior researcher. He is now affiliated with VSB – TU Ostrava and the IT4Innovations National Supercomputing Center. He is also a member of the IEEE, System, Man, and Cybernetics Society. He serves in the SMC Technical Committees on Soft Computing and Big Data Computing. He is also a member of steering commitee of the Neural Network World Journal and served in various chairing roles at several international conferences including WSC17 (PC Chair), IBICA 2013, 2014 (publicity co-chair), AECIA 2014, 2015 (publicity co-chair), IBICA 2015 (PC co-chair), INCoS 2013 (workshop co-chair), WSC18 (special events co-chair), etc.
Pavel’s areas of interest include computational intelligence, information retrieval, data mining, knowledge discovery, and parallel and distributed computing. He held courses on information retrieval and java programming and nowadays guarantees and teaches courses on parallel computing.