Underground Cable Fault Tracking by Ant Colony Optimization

Madichetty, Sreedhar; Roy, Subhadeep; Swain, Sarat Chandra; Routray, Aurobinda

doi:10.1109/delcon54057.2022.9752778

Cited by 1 publication

(1 citation statement)

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“…The use of ant colony optimization in resistor value estimation offers several advantages. It is a flexible [24] and adaptive approach [25] that can handle different resistor network configurations and accommodate variations in resistor characteristics. The algorithm's ability to leverage collective intelligence and distributed decision-making enables robust estimation even in the presence of noise or uncertainties in the circuit [26].…”

Section: Article Infomentioning

confidence: 99%

Ant Colony Optimization for Resistor Color Code Detection

Wibawanto,

Kirana,

Ramadhan

2023

Knowl. Eng. Data Sci.

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In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other local solutions. The first stage of testing focuses on exploring variations of parameter values. Applying the best parameters resulted in 85% accuracy and 43 seconds for 20 resistor images. This method is expected to prevent local solutions without wasteful computation of the matrix.

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Section: Article Infomentioning

confidence: 99%