Overview
Boids update each agent from nearby neighbors: separation avoids crowding, alignment matches heading, and cohesion steers toward the local group.
CoreSource checked
Boids Flocking
Simulates flocking from local separation, alignment, and cohesion rules.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Simulation
- Difficulty
- Beginner
- Family
- Agent-Based Simulation
- Tier
- Core
- Status
- Verified
- Review
- Source Checked
- Sources
- 1
- Formulas
- 1
- Relationships
- 2
- Code example
- Ready
- Visualization
- Ready
Core Concepts
local neighborhoodseparationalignmentcohesion
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(n)
Average
O(n^2)
Worst
O(n^2)
Space
O(n)
When to Use
- Use when plausible group motion is more important than physical accuracy.
- Use when local-neighborhood rules can produce the desired behavior.
When Not to Use
- Avoid when exact physical simulation or strategic planning is required.
- Avoid when neighbor search cost is too high without spatial acceleration.
What It Does
- Finds nearby agents
- Computes separation, alignment, and cohesion steering
- Updates velocity and position for each agent
Use Cases
- Flocking animation
- Crowd-like procedural motion
- Agent-based behavior demonstrations
Inputs
- Current state
- Forces or constraints
- Time step
Outputs
- Updated state
- Constraint corrections
- Collision results
Key Formulas (1)
Boid velocity
vnext = v + wsep·sep + walign·align + wcoh·coh
v_next = v + w_sep*sep + w_align*align + w_coh*cohCombines local steering rules into the next velocity.
Pseudocode
- 1.For each agent, find neighbors within the perception radius
- 2.Compute separation from very close neighbors
- 3.Compute alignment from average neighbor velocity
- 4.Compute cohesion toward average neighbor position
- 5.Clamp steering and update velocity and position
Canonical Example
- Input
- three agents, with one too close and two moving right
- Output
- the crowded agent steers away while still partly aligning right
- Explanation
- The final steering vector combines separation, alignment, and cohesion weights.
Implementation Notes
- Compute all steering forces from the previous frame before applying updates.
- Clamp velocity and steering magnitudes.
- Use a grid, quadtree, or spatial hash for larger flocks.
Common Pitfalls
- Large timesteps or steering weights can cause jitter or explosions.
- Naive all-pairs neighbor checks do not scale well.
- Updating agents in-place can make behavior depend on iteration order.
Interactive Mechanism Walkthrough
Iterative Convergence & State TrajectoryShow state updates, constraints, and error correction over simulation steps.
Step 1 of 5: For each agent, find neighbors within the perception radius
simulation-mechanismNotes
Boids are behavioral rules, so parameter tuning and deterministic update order matter more than physical correctness.
Sources (1)
What each reference supports for this entry.
Explore Next
Good next jumps from the relationship graph, ranked for useful discovery.
Relationships
View full relationship pageConceptual neighborN-Body Simulation
Both update many interacting agents or particles.
Conceptual neighborParticle Swarm Optimization
Both use local agent interactions to produce collective behavior.
Related Algorithms
Tags
SimulationFlockingAgent BasedParticles