Ant Colony Optimization

Technique

Ant Colony Optimization (ACO) is a metaheuristic optimization technique documented in the provided evidence as the basis for a Variant of ACO (VACO) used in the Instiller RTL fuzzer for input instruction distillation. The evidence also documents ACO being applied to automated software testing via state-transition testing, and to cryptographic Boolean-function design as a metaheuristic alongside simulated annealing and memetic algorithms.

First seen 5/26/2026

Last seen 7/11/2026

Evidence 7 chunks

Wiki v3

WIKI

Overview

In the provided evidence, Ant Colony Optimization (ACO) is used as a metaheuristic / approximate optimization technique. Its strongest direct application-level characterization comes from the Instiller RTL fuzzing work, where ACO is adapted into a Variant of ACO (VACO) that distills input instructions for CPU register-transfer-level (RTL) fuzzing. [C1][C2]

The evidence also documents ACO being applied in two other application domains: automated software testing (state-transition test-sequence generation) and cryptographic Boolean-function design (search for vectorial Boolean functions with low differential uniformity and high nonlinearity). [C3][C4]

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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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VACO variant_of The evidence explicitly names VACO as a variant of ACO introduced by the Instiller paper for RTL-fuzzing input instruction distillation.

CITATIONS

4 sources

4 citations — click to collapse

[1] Instiller is an RTL fuzzer based on ant colony optimization (ACO), and distills input instructions with a variant of ACO (VACO). Instiller: Toward Efficient and Realistic RTL Fuzzing | SCIENCE@home

[2] VACO-based distillation makes inputs shorter and more effective, generates 79.3% shorter input instructions than DiFuzzRTL, yields 29.4% more coverage, 17.0% more mismatches, and a 6.7% average execution-speed increase. Instiller: Toward Efficient and Realistic RTL Fuzzing | SCIENCE@home

[3] ACO is applied to state-transition testing to generate optimal and minimal test sequences and obtain complete software coverage, and is compared with genetic algorithms for transition-based testing. Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

[4] ACO is used alongside simulated annealing and memetic algorithms to design vectorial Boolean functions with low differential uniformity and high nonlinearity for cryptographic S-boxes. Using evolutionary computation to create vectorial Boolean functions with low differential uniformity and high nonlinearity