Self-organized Parallel Multi-objective Optimization

Abstract

In science and industry there are many optimization problems with multiple conflicting objectives which are of great complexity. In almost every discipline including engineering, biology, chemistry and physics, scientists deal with optimization problems.

Over the past decade, many new ideas have been investigated to solve different kinds of optimization problem. Any new development in optimization which leads to a better solution of a particular problem is thus of considerable value to science and industry.

This talk is about multi-objective optimization algorithms for solving discrete and continuous problems using evolutionary algorithms and particle swarm optimization. Additionally, it presents parallel optimization algorithms using a large number of parallel computing resources. Many parallel environments contain heterogeneous resources and are unreliable. In this talk I explain how the resources can operate in a self-organized way. Different issues for self-organized parallel resources are discussed. Self-organization of technical systems and their requirements are discussed using an application from mechanical engineering.

Biography

Sanaz Mostaghim is currently working as a lecturer and research assistant at Karlsruhe Institute of Technology in Karlsruhe, Germany. She received her PhD degree in electrical engineering from the University of Paderborn in Germany in 2004. After her PhD, she worked as a post doctoral fellow at the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland. She has worked on multi-objective optimization algorithms using evolutionary algorithms and particle swarm optimization and successfully applied them to several different applications from geology and computational chemistry. Parallel optimization, multi-objective optimization, particle swarm optimization, grid computing, and organic computing are her research topics.