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Social component

The social component is a particle-swarm parameter that weights attraction toward the best-known position of the swarm or a neighborhood.

How 2 Manning books explain it

Grokking AI Algorithms, Second Edition

The social component is the particle-swarm term that directs a particle toward information from the swarm's best-known solution.

How it is used
Directs particle-swarm movement toward the swarm’s best-known solution.
How deep it goes
Explains intuition, tuning effects, random scaling, coordinate-wise implementation, and local-optimum risk.
Examples
Uses a tourist analogy and a Python calculate_social function to illustrate group-directed movement.
What you can do after
Apply it via Python by scaling the swarm-best displacement with a social constant and random factor.

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Optimization Algorithms: AI techniques for design, planning, and control problems

A particle swarm optimization parameter that weights a particle's attraction toward the swarm's or neighborhood's best-known position.

How it is used
Guides particles toward collective best-known positions and helps balance exploration with exploitation.
How deep it goes
Defines the velocity-update role, coefficient effects, limiting cases, stability heuristic, and adaptive schedule.
Examples
Uses an amusement-park group-search analogy and a figure showing changing cognitive and social coefficients.
What you can do after
Operate/configure PSO by tuning c2 against c1 and scheduling stronger social influence during later iterations.

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