Swarm Intelligence
Discover how collective behavior emerges from simple interactions
Your Progress
0 / 5 completedFrom Biology to Computation
Observing nature's swarms inspired computer scientists to create optimization algorithms. These mimic biological behaviors to solve problems like pathfinding, scheduling, and parameter tuning— often outperforming traditional methods.
Three Foundational Algorithms
Interactive: Algorithm Convergence Simulator
Watch the swarm algorithm search for the optimal solution over iterations.
Particle Swarm Optimization (PSO)
1995Inspired by: Bird flocking and fish schooling
Particles search solution space, influenced by their best position and swarm best
ALGORITHM STEPS
COMMON USE CASES
Function optimization, neural network training, feature selection
Why Swarm Algorithms Work
Exploration vs Exploitation
Agents explore diverse regions while exploiting promising areas—avoiding local optima
Collective Memory
Swarm remembers best solutions found, guiding future search without central storage
Stochastic Search
Random elements prevent getting stuck, enable escape from suboptimal solutions
Parallel Processing
Multiple agents search simultaneously, dramatically faster than sequential methods
💡 Key Insight
Swarm algorithms trade precision for robustness. Traditional optimization finds exact solutions but fails if the problem changes. Swarm methods find "good enough" solutions quickly and adapt in real-time— perfect for dynamic, noisy, or incompletely defined problems where classical methods struggle.