The moth into flame optimization algorithm (MFO) is a meta-heuristic algorithm that solves various optimization problems by imitating the movement of moths. The algorithm has been widely used in fields such as power and energy systems, economic dispatch, engineering design, image processing, and medical applications.
At night, moths often gather around lights. This is due to the fact that they rely on a mechanism of lateral positioning for specialized navigation. Moths need a distant light source to fly in a straight line, and they will maintain a fixed angle to the light source. Although lateral positioning is effective, moths are often observed flying in a spiral around the light. This is because moths are tricked by artificial light, causing them to exhibit this behavior. In order to maintain a constant angle to the light source, the moth will eventually circle around the light source.
In the Moth to Flame Optimization Algorithm ( In MFO), the candidate solution is assumed to be a moth, and the problem variable is the position of the moth in space. Therefore, moths can fly through space by changing their position vector.
It is important to note that both moths and flames are solutions, but they are processed and updated differently in each iteration.
The moth is a position that moves in the search space, and the flame represents the best position of the moth obtained so far. In other words, the flame can be seen as a central guiding point for the moths in their search, around which each moth searches and updates as it finds a better solution. This mechanism allows the moth algorithm to always maintain an optimal solution.
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