From 890b88de7e7c63dd9cd8c8611b34b8d60e8625b3 Mon Sep 17 00:00:00 2001 From: Paht J Date: Wed, 17 Jul 2024 10:53:23 -0400 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 34339596..29332c78 100644 --- a/README.md +++ b/README.md @@ -4,11 +4,11 @@ > `pip install glennopt` ## Objective -The objective of this project is to develop a standalone optimization tool that can be easily integrated into openmdao at a later stage. +The objective of this project is to develop a standalone multi-objective optimization tool that handles failed simulations and can be used to spam the cluster. This tool will be entirely written in python making it compatible with windows and linux. ## Why this tool? -This tool overcomes some of the limitations of gradient based optimization where F(x) = y which isn't always true for all simulations. GlennOPT uses a variation of Genetic Optimizers called Differential Evolution (DE). DE is capable of handling failed simulations. In the event of a failure the objective is set to a high value making the individual" undesireable for mutation and crossover. The other reason why someone would use this tool is if they had other parameters besides the objective that they wanted to keep track of or constrain. A good example of this is optimizing turbomachinery. There are many other parameters other than efficiency and power that matter. You might want to keep track of the mach number entering and exiting the geometry, flow angles, just to name a few. If you need gradient optimization, I recommend OpenMDAO or scipy minimize. +This tool overcomes some of the limitations of gradient based optimization where F(x) = y which isn't always true for all simulations. GlennOPT uses a variation of Genetic Optimizers called Differential Evolution (DE). DE is capable of handling failed simulations. In the event of a failure the objective is set to a high value making the individual" undesireable for mutation and crossover. The other reason why someone would use this tool is if they had other parameters besides the objective that they wanted to keep track of or constrain. A good example of this is optimizing turbomachinery. There are many other parameters other than efficiency and power that matter. You might want to keep track of the mach number entering and exiting the geometry, flow angles, just to name a few. I recommend scipy minimize if you need something Gradient based. OpenMDAO is a wrapper for scipy, just use scipy. ## Summary Many optimization packages seem like a compile of past tools written in other languages, they lack universal features described above that can make big data really happen at Glenn