Cognitive component
A particle-swarm parameter controlling the influence of a particle’s attraction toward its own best-known position.
Grokking AI Algorithms, Second Edition
The cognitive constant is a positive particle-swarm parameter below two that controls the influence of a particle's personal best position on its movement.
- How it is used
- Controls a particle’s attraction to its personal best during particle-swarm position updates.
- How deep it goes
- Defines the parameter, contrasts settings, and shows its coordinate-level Python calculation without deriving the full update formula.
- Examples
- Uses loner and trend-follower swarm analogies plus a Python calculation based on personal-best and current coordinates.
- What you can do after
- Apply it via Python by calculating personal-best attraction as part of each particle’s updated velocity.
Read how Grokking AI Algorithms, Second Edition explains cognitive component →
Optimization Algorithms: AI techniques for design, planning, and control problems
A particle swarm optimization parameter that weights a particle's attraction toward its own best-known position.
- How it is used
- Shapes particle movement by weighting attraction toward each particle’s personal best during particle swarm optimization.
- How deep it goes
- Defines c1, contrasts it with c2, gives boundary cases and tuning guidance, and stops short of deriving update formulas.
- Examples
- Uses amusement-park exploration and a visualization of motion components to illustrate personal-best attraction.
- What you can do after
- Apply it by tuning c1 against c2 to emphasize individual exploration, social exploitation, or an adaptive transition.
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.
- best positionuses · A best position is the position at which an optimization particle has achieved its best objective value so far.
- data explorationconfigures · Data exploration is the systematic examination and summarization of data to understand its structure, quality, distributions, relationships, patterns, and anomalies.
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