Consider this unique approach: implementing an iterative search technique utilizing a simulated repulsive force.
Strategy
To begin, set up the dataset by positioning the circles on the surface in an algorithmic manner for initialization purposes. This initial arrangement does not need to be precise, so a simple periodic table layout will suffice. Additionally, assign each circle a "mass" equivalent to its radius.
Next, start the iteration process in order to converge towards a solution. For each cycle through the primary loop, follow these steps:
Calculate repulsive forces for each circle, resembling the formula for gravitational force but with adjustments: (a) objects are repelled rather than attracted, and (b) adjust the "force constant" according to the model's scale. It may be beneficial to pre-determine a suitable constant or experiment initially to find an optimal value.
Upon determining the total forces on each circle (refer to the n-body problem if needed), displace each circle along the direction of the total force vector, with the vector's magnitude as the distance to traverse. It might be necessary to fine-tune the force constant to ensure movements are less than 5% of the circle's radius initially.
Due to the movements in the previous step, circles may be pushed off the surface of the sphere. Return each circle to the surface, moving towards the sphere's center.
Compute the distance between the circle's previous and new positions to determine the iteration's movement length.
Continue iterating through the main loop until movement length diminishes, indicating stabilized circle positions that meet the criteria. Terminate the loop when the movement length falls below a minute threshold.
Adjustments
To achieve convergence, adjustments to the force calculation may be necessary. The modifications depend on the desired outcome. Initially, modify the force constant. If ineffective, adjust the mass values or alter the exponent in the force calculation. For instance, instead of:
f = ( k * m[i] * m[j] ) / ( r * r );
Consider experimenting with:
f = ( k * m[i] * m[j] ) / pow( r, p );
Explore varying values for p during experimentation.
Additionally, test out alternate algorithms for the initial arrangement.
The level of trial and error will hinge on your specific design objectives.