aco | A C Ant Colony Optimization algorithm

This is my first post here and I am not that experienced, so please excuse my ignorance.

I am building a Monte Carlo simulation in C++ for my PhD and I need help in optimizing its computational time and performance. I have a 3d cube repeated in each coordinate as a simulation volume and inside every cube magnetic particles are generated in clusters. Then, in the central cube a loop of protons are created and move and at each step calculate the total magnetic field from all the particles (among other things) that they feel.

At this moment I define everything inside the main function and because I need the position of the particles for my calculations (I calculate the distance between the particles during their placement and also during the proton movement), I store them in dynamic arrays. I haven't used any class or function,yet. This makes my simulations really slow because I have to use eventually millions of particles and thousands of protons. Even with hundreds it needs days. Also I use a lot of for and while loops and reading/writing to .dat files.

I really need your help. I have spent weeks trying to optimize my code and my project is behind schedule. Do you have any suggestion? I need the arrays to store the position of the particles .Do you think classes or functions would be more efficient? Any advice in general is helpful. Sorry if that was too long but I am desperate...

Ok, I edited my original post and I share my full script. I hope this will give you some insight regarding my simulation. Thank you.

Additionally I add the two input files

parametersDiffusion_spher_shel.txt

parametersIONP_spher_shel.txt

The topic link: https://codeforces.com/problemset/problem/600/D

For the question, I'm wrong answer on test28, which could look like this:

correct answer:119256.95877838134765625000

my answer: 120502.639190673828125

I guess it is caused by calculation accuracy, but I don't have evidence. Maybe algorithm itself is faulty, please point it out.

Algorithm ideas:

For any given two circles, in order to simplify the calculation, we can translate the origin of the coordinates to the center of one of the circles, and then rotate the other circle to the x-axis by rotating. For calculating the intersection area of the circles, before and after are equivalent, and finally a purple circle and a red circle are formed. In fact, the final intersection area is equal to the sum of the areas of the two sectors minus the area of the diamond in the middle(the figure below, Horizontal axis x, vertical axis y). However, before that, we must first calculate the intersection point of the two circles.

The coordinates of the center of the first circle at the beginning: .

The coordinates of the center of the second circle at the beginning: .

The coordinates of the center of the first circle after a series of transformations: .

The coordinates of the center of the second circle after a series of transformations: ,

.

The equations of two circles are combined:

are the radius of the first and second circles respectively,so:

we can use the sector area formula : ,

, .

In this place, there will be problems with the positive and negative values of the radian(the two figures below), but it can be proved that they can be absorbed in the final result.

The final result is the sum of the areas of the two arcs minus the area of the middle diamond.

mycode:

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I've been trying to figure out the 2D rotation value as seen from orthographic "top" view for a 3D object with XYZ rotation values in Maya. Maybe another way to ask this could be: I want to figure out the 2D rotation of a 3D obj's direction.

Here is a simple image to illustrate my question:

I've tried methods like getting the twist value of an object using quaternion (script pasted below), to this post I've found: Component of a quaternion rotation around an axis.

If I set the quaternion's X and Z values to zero, this method works half way. I can get the correct 2D rotation even when obj is rotated in both X and Y axis, but when rotated in all 3 axis, the result is wrong.

I am pretty new to all the quaternion and vector calculations, so I've been having difficulty trying to wrap my head around it.

;)

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This is my data:

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A sample dataset for the problem:

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Routes