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Particle

A particle is an individual entity represented or managed as a unit within a physical or computational model.

How 2 Manning books explain it

Grokking AI Algorithms, Second Edition

A particle is a candidate solution in particle swarm optimization whose position represents values in the problem search space and whose movement is adjusted during search.

How it is used
Represents candidate solutions moving through a multidimensional search space in particle swarm optimization.
How deep it goes
Defines particle state and walks through initialization, fitness evaluation, movement, and stopping with Python examples.
Examples
Uses aluminum and plastic choices in a drone-design search, particle-state tables, and Python snippets.
What you can do after
Build it from scratch as a Python particle with position, velocity, fitness, and remembered best values.

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Evolutionary Deep Learning: Genetic algorithms and neural networks

A particle is a candidate agent in particle swarm optimization whose position and movement encode a possible solution.

How it is used
Uses particles as candidate solutions while applying particle swarm optimization to an equation-solving problem.
How deep it goes
Explains generation, fitness evaluation, personal and global bests, speed limits, and position updates through executable functions.
Examples
Uses cannon-shot velocity and angle, target distance, swarm scatter plots, and DEAP notebook listings.
What you can do after
Apply it via DEAP by generating, evaluating, updating, and inspecting candidate solutions in a swarm.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.