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In this post, we’ll be exploring the application of Dispersive Flies Optimisation, as originally pondered in my previous post. Specifically, we’ll discuss applying DFO to AirBnB data, as the AirBnB data is readily available with very little effort. I will be referring to the data provided for London, however all of the available data should be the same.

There are probably loads of ways we can apply DFO to search this information; I’m going to be looking for the best place to stay.

It’s quite curious that at the time of writing, this algorithm doesn’t have so much as a Wikipedia page. Heck, a cursory Google search implies that the individual who came up with it is the very same guy who asked for people to think about it.

Yeah, you! I know you’re out there Mohammad!

Dispersive Flies Optimisation

page1-800px-sphere_function_in_3d-pdfDispersive Flies Optimisation (DFO) is a swarm intelligence algorithm that aims to find the best piece of data in a matrix. How good a piece of data (referred to as an agent) is, is judged by its fitness. An agent contains data, or I suppose metadata, such as a location, among other possible things. The flies are then scattered across various locations within the data and set to finding the optimal location, which is either the lowest possible fitness, or highest.