Inertia component
The inertia component is the particle-swarm velocity term that weights a particle’s previous velocity during movement updates.
Grokking AI Algorithms, Second Edition
The inertia component is the particle-swarm velocity term that preserves a particle's previous movement momentum.
- How it is used
- Controls whether particle-swarm search emphasizes exploration or exploitation during velocity updates.
- How deep it goes
- Defines the parameter and traces its velocity contribution, but defers the complete update to cognitive and social terms.
- Examples
- Uses freight-truck and housefly analogies plus a Python function multiplying current velocity by inertia.
- What you can do after
- Build it from scratch by calculating an inertia contribution from a particle’s current velocity.
Read how Grokking AI Algorithms, Second Edition explains inertia component →
Optimization Algorithms: AI techniques for design, planning, and control problems
The inertia component is a particle swarm optimization parameter that weights a particle's previous velocity when updating its movement.
- How it is used
- Balances exploration and exploitation by preserving a particle’s movement within particle swarm optimization.
- How deep it goes
- Explains inertia as one velocity-update component and relates it to position updating, but gives no parameter-tuning procedure.
- Examples
- Uses fish and bird movement analogies and a two-dimensional vector visualization of the motion equation.
- What you can do after
- Evaluate/understand how inertia contributes to particle velocity and search behavior.
Related concepts
- particle swarm optimizationused by · Particle swarm optimization is a population-based optimization method in which candidate solutions adjust their positions using individual and collective search information.
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