Swarm intelligence · fitness landscape
A swarm foraging a landscape
A flock of agents forages a multimodal fitness landscape — brighter regions are higher fitness. Local flocking rules combine with a particle-swarm pull toward each agent’s own best and the globally best-known peak, so the swarm converges on high ground without any agent seeing the whole map.
Research pillar · Computational Intelligence & Optimization
This interactive is an illustrative stand-in — a simplified model built to convey the idea and behaviour behind this research area for teaching. It is not a specific published method, dataset, or result.
How it works
Every agent remembers the best spot it has personally found and is told the best spot any agent has found. Its velocity blends three pulls — inertia, a tug toward its personal best, and a tug toward the global best — plus light flocking with nearby agents. Sampling the landscape as they move, agents update those bests continuously, so the whole swarm climbs toward the tallest peak while still exploring. Reset for a new random landscape.
More demos
These illustrate the research areas behind my work — see the research pillars for the full picture, or the crowd-evacuation simulation for a demo built directly from published papers.