modeFRONTIER Optimization Algorithms
Course overview
modeFRONTIER Optimization Algorithms gives the deep knowledge and technical details of all optimization algorithms available in the current version of modeFRONTIER. A detailed description of mono-objective and multiobjective optimization algorithms and several mathematical and industrial examples will teach the attendee the best optimization strategy to tackle, in the most profitable way, most complicated optimization problems.
Prerequisites
Getting started with modeFRONTIER or modeFRONTIER Fundamentals are recommended to get the best from this course.
Audience
The primary audience for this course includes skilled modeFRONTIER users that want to have a complete and detailed explanation of all modeFRONTIER optimization algorithms to tackle every optimization problem, even the most complicated one, in the most profitable way.
Topics
- Introduction to Global Optimization
- Differences between Single and Multiobjective optimizations
- Introduction to Design of Experiments (DOE) and Statistical analysis
- Single-objective and Multiobjective optimization methods
- Gradient Based Methods
- Sequential Quadratic Programming
- Non-Gradient Based Method (Nelder Mead Simplex)
- Evolutionary Algorithms
- Single and Multiobjective Simulated Annealing
- Genetic Algorithms
- Game Theory based optimizers - Basics of Response Surface Methods (RSM)
- Optimization strategies
- Optimization Post-processing
- Multi-Criteria Decision Making
At the end of the course, the attendee will be able to
- Choose the most appropriate optimization method for the problem at hand
- Interpret multiobjective optimization outcomes
- Speed up the optimization process taking advantage of statistical studies and response surface methodologies