Finding the Global Minimum of a Function using Simulated Annealing

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Hi Housam,

That is a great job. Congratulations.

Archimedes optimization algorithm is a new and most useful in engineering.
I have its code in Matlab, but I would like to have it in Mathematica.
I am new in Mathematica and don’t have experience for providing it code. I know you are so busy, so may I ask you to do that in Mathematica?

Best Regards,
Alex

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thanks. OK kindly share AOA Matlab coding with me and I will do it using MATHEMATICA

binoushousam@yahoo.com

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Hi Housam,

Minimize and NMinimize are known to give global solution unless some warning was given. You can find this stated in the “Details and Options” section of their documentation pages.

Which version of Mathematica are you using?

Using V13.3 I get the global solution by both

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I am using v.12. Thanks for the info

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This is true of Minimize. But NMinimize can get trapped in a local min. In this example of course it does just fine:

In[109]:= NMinimize[f[x], x, Method -> "SimulatedAnnealing"]

Out[109]= {-3.0233, {x -> -0.711232}}

Also one should bear in mind that any method based on simulated annealing can get trapped. It just doesn’t happen for this example and the implementations presented.

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Dear Housam,

the AOA code in Matlab has been sent to your email address.

Regards

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thanks, I have received the code and I will have a look at it the coming days

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Hi Housam,

I attached herewith the AOA source code.

Thank you .

AOA.m (2.87 KB)
main.m (347 Bytes)
sumsqu.m (315 Bytes)

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Dear Housam,

Have you received the AOA files?

Looking forward to hearing your reply.

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I am tied up with other stuff. Eventually, I will get to it. Please use my Email to contact me.
Best regards,
Housam