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Pheromone update

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

Pheromone reinforcement increases simulated pheromone levels on solution components selected by high-quality ant solutions.

Pheromone update reinforces solution components chosen by ants. Each update evaporates existing pheromone and deposits new pheromone, so better paths gradually attract more ants while evaporation preserves exploration. section 10.2 section 10.6

A practical update proceeds in these stages:

  • Initialize pheromone values, often equally across paths or edges. section 10.2
  • Construct ant solutions using pheromone concentration and, where applicable, transition probabilities or heuristic information. section 10.2 section 10.3.3
  • Evaporate pheromone by decreasing its concentration at rate ρ; low evaporation can trap the search, while maximum evaporation forces random exploration. section 10.2
  • Deposit pheromone on selected components, either during construction or after evaluating a completed solution. section 10.6
  • Choose the depositing ants according to the ACO variant: ACS uses the global-best solution, while MMAS uses the iteration-best path and bounds pheromone values. section 10.3.3 section 10.6

Shortest-path update

Suppose an ant constructs the path 1 → 3 → 4 → 5 with length 11. With Q = 1, delayed deposition reinforces edges {1,3}, {3,4}, and {4,5} by Q/L = 1/11 after evaporation. Repeating this process over three iterations identifies the shortest path in the toy graph. section 10.3.1

Figure 10.5 Pheromone update methods

Compare online step-by-step and delayed pheromone update methods in a single visual overview. section 10.2

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

How deep it goes
Explains initialization, construction, evaporation, deposition, update timing, and variant-specific selection through a worked shortest-path example.
Examples
Uses two-path foraging, a NetLogo ant simulation, and a five-node shortest-path graph with computed deposition.
What it assumes
Assumes basic graph paths and probabilities while teaching pheromone concentration, evaporation, deposit, and ACO update variants.
What you can do after
Apply it via an ACO implementation by balancing evaporation, deposition timing, solution quality, and exploration.
How it connects
Connects evaporation and deposit with ant colony optimization, exploration and exploitation, transition probabilities, ACS, AS, and MMAS.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.