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Project-Platypus / Platypus

A Free and Open Source Python Library for Multiobjective Optimization
GNU General Public License v3.0
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speed concerns #88

Closed quaquel closed 1 year ago

quaquel commented 5 years ago

I am playing around with platypus and the inter temporal lake problem. I have implemented a generational version of BORG using MultiMethod and NSGA2. I however now run into severe run problems. For example 60% of my runtime is spend in the orthogonalize function. A quick look at the code easily explain why: everything is done using lists.

Hence my question: are there specific reasons for not using numpy? A simple implementation of orthogonalize using numpy with the inter temporal lake problem (so 100 decision variables) is a factor 10 faster.

github-actions[bot] commented 2 years ago

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