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Pheromone

A pheromone is a chemical or simulated signal whose strength conveys trail preference and influences subsequent route choices.

How 2 Manning books explain it

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

A pheromone concentration is a numerical measure of trail strength assigned to an edge to influence subsequent ant transitions in an ant-colony algorithm.

How it is used
Guides probabilistic route choices in ant colony optimization by carrying information from earlier ants to later ones.
How deep it goes
Works through edge initialization, transition weighting, evaporation, and cost-based deposits, without deriving the transition formula in the supplied excerpts.
Examples
Uses a five-ant shortest-path example and Python code that updates pheromone levels between cities on an open traveling-salesman route.
What you can do after
Build it from scratch by weighting feasible transitions with pheromone and distance, then evaporating and depositing trail values after constructed routes.

Read how Optimization Algorithms: AI techniques for design, planning, and control problems explains pheromone →

Grokking AI Algorithms, Second Edition

Pheromone intensity is the numerical strength of a simulated pheromone signal associated with a path in ant colony optimization.

How it is used
Guides route selection in ant colony optimization by recording and sharing the influence of paths explored by simulated ants.
How deep it goes
Works through equal initialization, probability-based selection, evaporation, and tour-based reinforcement with Python snippets, without pursuing a theoretical proof.
Examples
Uses a short-versus-long bridge, a carnival attraction matrix, and Python trail initialization to make changing path strengths concrete.
What you can do after
Build it from scratch by initializing trail intensities, using them in next-visit choices, and updating them after tours.

Read how Grokking AI Algorithms, Second Edition explains pheromone →

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