An Introduction to Swarm Intelligence Algorithms
summary
Overview of concrete algorithms and runnable C++ code that moves particles, ants, bees and cats to solve toy optimizations; demos implement particle swarm and cat swarm optimization with positions, velocities, a seeking mode (J≈5 candidates) and a tracing mode (mixture ratio ~10%), plus ant-style pheromone graphs and an rosenbrock benchmark. Code is refactored for testability, uses weighted (roulette) selection and velocity clamping, and visually herds blobs out of a paper bag — demos mostly work though embedded slides sometimes fail.
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chapters (12)
- 0:00Swarm intelligence intro
- 1:17What is swarm intelligence
- 3:29Randomness and stochastic methods
- 13:31Particle swarm optimization demos
- 14:34Ant colony pathfinding
- 17:29Bee-inspired swarm behavior
- 23:47Cat swarm optimization
- 41:23Cat swarm seeking mode
- 42:08Tracing mode and velocity
- 53:54Testing with pathological functions
- 58:32Summary and demos
- 1:02:03Live demos and Q&A
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