Cellular Automata Vector Swarm v3.1.0

2,000 Bacterial Boids Swarming into Bad Apple Silhouettes

An autonomous particle simulation where 2,000 bacterial agents follow Craig Reynolds flocking heuristics (Separation, Alignment, Cohesion) coupled with dynamic spring-damper vector attraction fields. Witness smooth shape-morphing transitions, shockwave dispersal explosions, and real-time spatial correlation telemetry.

Active Boids 2,000

Alignment Fidelity 98.4%

Flock Coherence 0.962

Frame Time 16.6 ms

Monochrome Phosphor Screen

2,000 Particles @ 60 FPS

Boids Swarm Dynamics

Target Spring Attraction 1.4x

Separation Force 1.2x

Max Swarm Velocity 5.5 px/f

Phosphor Trail Decay 0.25

Visual Rendering Style

Interactive Controls

Real-Time Spatial Correlation & Swarm Coherence Sweep

60 FPS Dynamic Vector Calculus

Reynolds Flocking & Spatial Attractors

Particle steering combines classical boids separation $\vec{F}_{\text{sep}}$, alignment $\vec{F}_{\text{ali}}$, and target spring field $\vec{F}_{\text{target}}$:

$\vec{a}_i = w_s \sum_{j \ne i} \frac{\vec{x}_i - \vec{x}_j}{\|\vec{x}_i - \vec{x}_j\|^2} + w_a (\bar{\vec{v}} - \vec{v}_i) + k_t (\vec{T}_i - \vec{x}_i) - c \vec{v}_i$

Vector Silhouette Sampling

Complex silhouette contours (Apple, Touhou witch hat, DNA helix, Skull) are discretized into 2,000 analytical Poisson-distributed coordinate targets $\vec{T}_i \in \mathbb{R}^2$.

$\Phi(t) = (1 - \lambda)\vec{T}_A + \lambda \vec{T}_B, \quad \lambda \in [0, 1]$

Spatial Fidelity Metric

Real-time alignment fidelity $C(t)$ quantifies the mean normalized Euclidean distance between active boids and their designated silhouette anchors:

$C(t) = 100 \times \left(1 - \frac{1}{N}\sum_{i=1}^N \frac{\min(\|\vec{x}_i - \vec{T}_i\|, R_{\text{max}})}{R_{\text{max}}}\right)$