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From Brain Science to Intelligent Machines

Genetic and swarm optimisation algorithms for modelling and control of dynamic systems

Date: Wednesday 14/11/2012
Venue: MS020
Time: 1.00 pm
Speaker: Dr. Shafiul Alam
Affiliation: University of Dhaka, Bangladesh


Genetic and swarm optimisation algorithms for modelling and control of dynamic systems

By Dr. Shafiul Alam
University of Dhaka, Bangladesh


In evolutionary optimisation, genetic algorithms (GAs) and Particle swarm optimisation (PSO) are highly relevant for research and industrial applications, because they are capable of handling problems with multimodal characteristics, non-linear constraints and dynamic properties. On the other hand, a wide range of real-world problems requires multiple design objectives and constraints which are often competing in nature to be satisfied simultaneously. In such cases, the multi-objective GA or PSO algorithms can provide a set of trade-off solutions to the problem’s conflicting objectives in a single run. Here few modelling and control problems (of Robotics & Systems Biology) are discussed where single & multi-objective GA/PSO algorithms have been used as an alternative to conventional approaches.

Short biography:

Shafiul Alam is a Commonwealth Fellow in School of Computing, Engineering & Information Sciences, Northumbria University, Newcastle upon Tyne, under Commonwealth Fellowship Programme. He received his PhD from Department of Automatic Control and Systems Engineering, University of Sheffield, UK, in 2007 under same fellowship scheme. He also worked as a post-doc researcher in School of Computing, Informatics and Media, University of Bradford, UK, under an EU Erasmus Mundus project in 2009-2010. He completed B.Sc (Hons) and M.Sc degrees in Applied Physics and Electronics in 1995 and 1997 respectively from University of Dhaka, Bangladesh and currently he is an Associate Professor in the same department. His research interests include evolutionary algorithms, swarm intelligence, fuzzy logic control and artificial neural networks and its applications to modelling and control of dynamic flexible systems and Systems Biology.