Analysis of Intelligent Approaches for Discovery and Management of Knowledge: A Review

ahahmad, rayees; MISHRA, KAMTA NATH

doi:10.2139/ssrn.4161379

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2023

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“…The pheromone trails guide the ants towards the nodes with higher pheromone concentration, which are likely to correspond to the locations of the resistor rods. Meanwhile, the heuristic information provides additional guidance by incorporating domain-specific knowledge or constraints into the decision-making process [22].…”

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%