An Introduction to Swarm Intelligence Algorithms
summary
Implements a cat swarm algorithm in C++ with working demo code (50/50 chance the PowerPoint embed runs), uses C++11 randoms and SFML for real‑time visuals, shows cats with two roles — seeking (samples five nearby positions, 2.5× max step) and tracing (velocity nudged toward global best, weight ≈1) — and handles objective functions like Rosenbrock and a plateau distraction. Uses a weighted discrete_distribution with a uniform fallback, hardcoded mixture ratios (eg two tracing eight seeking) and logs best value as cats converge.
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chapters (13)
- 0:00Introduction
- 1:47Swarm intelligence algorithms
- 6:19Swarm vs other AI methods
- 9:12Particle swarm optimization
- 12:51Ant and bee algorithms
- 16:01Cat swarm algorithm
- 23:13How seeking and tracing cats work
- 31:23Avoiding local minima
- 34:11Hard optimization functions
- 37:24Summary
- 40:15Real-world applications
- 43:31Live demo: cat swarm
- 51:13Simple loops solve problems
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