Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Poster - Department of Information Technology
Document related concepts
Lateral computing wikipedia , lookup
Mathematical economics wikipedia , lookup
Factorization of polynomials over finite fields wikipedia , lookup
Algorithm characterizations wikipedia , lookup
Computational phylogenetics wikipedia , lookup
Molecular dynamics wikipedia , lookup
Reinforcement learning wikipedia , lookup
Natural computing wikipedia , lookup
Multiple-criteria decision analysis wikipedia , lookup
Theoretical computer science wikipedia , lookup
Simulated annealing wikipedia , lookup
Computational fluid dynamics wikipedia , lookup
Computational chemistry wikipedia , lookup
Multi-objective optimization wikipedia , lookup
Transcript
Parallel Optimization with Matlab 1. Furnaces We conclude that MultiStart is the most promising method for further studies. It is fast, consistently finds the best optimum and shows good parallel speedup. 4. Runout Table 2. Reversing Roughing Mills 5. Coiler 3. Finishing Mill Joakim Agnarsson [email protected] Hybrid SA scales well and is reliable in finding the optimum, but it is slower than MultiStart. Industrial hot-rolling is an energy-intensive and time-consuming process used to create metal products such as sheets. Inna Ermilova [email protected] The machine settings are called the rolling schedule. As the schedule is described by mathematical models, it can be optimized to reduce costs and improve the product quality. Patternsearch is equally accurate as MultiStart but does not scale as well for larger clusters. Only some of the methods are of interest for highly parallel calculations GlobalSearch is accurate but slower than MultiStart and has no parallelism. Genetic algorithms are too slow and do not converge to a good optimum. The long-term goal is to perform schedule optimization in real time during production. Comparing methods Mikael Sunde [email protected] Kateryna Mishchenko Supervisor ABB Corporate Research [email protected] Maya Neytcheva Course coordinator Department of Information Technology, Uppsala University [email protected] A Matlab framework is implemented which makes it easy to use the built-in global optimization methods in an efficient parallel way. With this framework we investigate the optimization methods in terms of speed, accuracy and parallel speedup. This is done on a ”black box” problem – the objective function is unknown but given – with nonlinear inequality constraints. Project in Scientific Computing, 2012 we find significant differences between them. The framework makes it easy to use the optimization functions in an efficient and parallel way. Method GlobalSearch The methods we investigate are (i) GlobalSearch (ii) MultiStart (iii) Genetic algorithm (iv) Hybrid simulated annealing (Hybrid SA) (v) Patternsearch MultiStart Genetic algorithm Hybrid SA Patternsearch Serial Parallel Accuracy Speed Speedup
