Ant Colony Optimization

Technique

Ant Colony Optimization (ACO) is described in the provided evidence as an approximate optimization technique inspired by ants searching for shortest paths. The evidence documents ACO itself and a variant of ACO (VACO) used by Instiller for RTL fuzzing input distillation, and it also shows ACO applied in automated software testing and in cryptographic Boolean-function design.

First seen 5/26/2026

Last seen 6/3/2026

Evidence 6 chunks

Wiki v2

WIKI

Overview

In the provided evidence, Ant Colony Optimization (ACO) is described as an approximate optimization technique whose algorithm simulates an ant colony searching for the shortest path to a target city. This is the clearest direct characterization of the technique in the source set. [C1]

Evidence-supported characterization

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RELATIONSHIPS

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Variant of Ant Colony Optimization ← derived from 100% 11e

The Variant of Ant Colony Optimization (VACO) is derived from classic Ant Colony Optimization with modifications for RTL fuzzing.

INSTILLER ← uses 100% 5e

Instiller is based on ant colony optimization for distilling input instructions.

VACO ← derived from 100% 3e

VACO is a variant of ACO (Ant Colony Optimization).

Input Instruction Distillation ← derived from 1e

Input instruction distillation is derived from the ant colony optimization technique.

LINKED ENTITIES

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Variant of Ant Colony Optimization DERIVED_FROM Extracted graph relationship

CITATIONS

5 sources

5 citations — click to expand

[1] ACO is described as an approximate optimization technique whose algorithm simulates an ant colony searching for the shortest path to a target city. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing

[2] Instiller uses the idea of ACO for input instruction distillation, modeling instruction length as ants and RTL circuits as cities, and introduces a variant of ACO (VACO) for RTL fuzzing. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing

[3] Instiller reports 29.4% higher coverage, 79.3% shorter inputs than DiFuzzRTL, 17.0% more mismatches, and a 6.7% average execution-speed increase from instruction distillation. [2401.15967] Instiller: Towards Efficient and Realistic RTL Fuzzing

[4] A paper applies ant colony optimization to state-transition software testing to generate optimal and minimal test sequences and achieve complete software coverage. Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

[5] A paper reports experiments using ant colony optimisation, simulated annealing, and memetic algorithms to create vectorial Boolean functions with low differential uniformity and high nonlinearity. Using evolutionary computation to create vectorial Boolean functions with low differential uniformity and high nonlinearity