Find Desired Amount of Furthest Away Objects From Each Other

A method for selecting a specific number of objects that maintain maximum spacing from one another using a spatial sampling algorithm.

This method provides a way to select a specific number of objects that are spread out as evenly as possible across an area. By utilizing a variation of "Farthest Point Sampling," the algorithm ensures that every selected object maintains maximum "breathing room" from the rest of the group.

Spatial Sampling Logic

A common mistake when attempting to find widely spaced objects is searching for the "maximum distance to any single object." This can result in the algorithm selecting a point that is very far from one existing object but touching another.

To achieve proper distribution, the logic must instead find the point whose closest neighbor is as far away as possible from the entire existing set. This is known as Maximizing the Minimum Distance.

Implementation Workflow

The algorithm builds the set one point at a time through a three-level nested loop structure.

1. Setup: Use Get All Spawn Points (or a similar object retrieval node) to populate an All Points list. Use Create Empty List to initialize a Selected Points list and define a Target Count.

2. Initial Selection: Use Get Random Item from All Points to add the first point to the Selected Points list, then use Remove Item to take it out of All Points.

3. Main Loop: While the Count of Selected Points is less than the Target Count, perform the following:

• Outer Loop: For each Candidate in All Points:

• Inner-Inner Loop: For each Selected Point in Selected Points:

• Calculate the Distance between the Candidate and the Selected Point.

• Track the smallest distance found; this is the candidate's "safety buffer."

• Comparison: Once the Inner-Inner Loop finishes, check if this candidate's smallest distance is greater than the current Max Min Distance. If it is, update Max Min Distance and set this candidate as the Best Candidate.

• Finalize Round: After the Outer Loop completes, add the Best Candidate to Selected Points and remove it from All Points.

The node graph illustrates the nested loop structure required for this sampling logic.

This image shows a successful distribution of objects throughout the area.

The selected objects are spread across the available space.

Variable Management

To ensure the algorithm correctly identifies the most isolated candidate, variables must be reset at different stages of the loop hierarchy.

Variable	Reset Value	When to Reset	Purpose
Max Min Distance	0	Start of the Main Loop	Tracks the largest "safety buffer" found during the current selection round.
Min Distance	10000	Start of the Outer Loop	Tracks the distance to the closest neighbor for the current candidate.

Using a very high number (like 10000) for the initial Min Distance ensures that the first distance calculated for a new candidate will always be smaller than the starting value.

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Source Data

• Discord thread: Find Desired Amount of Furthest Away Objects From Each Other

Contributors

Okom Guild Archivist swagonflyyyy (Mr. Blackwell)