Ant Colony Optimisation – An Explanation

In this blog, I will be explaining how the technique Ant Colony Optimisation works and where this can and has been applied. What is Ant Colony Optimisation? Ant Colony Optimisation (ACO) is a method that attempts to simulate how ants behave when deciding on the optimal path. This method is typically used on networking solutions, […]

An Application for Stochastic Diffusion Search

Google Images - Search by Image

Following from my post last week, I’ll be talking about an example application for Stochastic Diffusion Search, otherwise known as SDS. I believe that this can be used in many different ways, but I will explain how I think it could be implemented in a search by image algorithm. If you do not know how […]

SDS – An Explanation

What is SDS? Stochastic Diffusion Search (SDS) is a search algorithm that using swarm intelligence to achieve its outcome. The algorithm contains agents, that each have a hypothesis and a current state, and each of them communicate with each other. How does it work? In order for the swarm to find the model they are […]

Dispersive Flies Optimisation

What is Dispersive Flies Optimisation? Dispersive Flies Optimisation (DFO) is a swarm intelligence model based on how flies interact in the real world. From a paper produced by Mohammad Al-Rifaie, this has been created and used to benchmark different test functions for optimisation. The progress of the flies optimisation is based on a value called […]

“No Free Lunch” Theorem

The “No Free Lunch” Theorem is the idea that every problem cannot be solved by a single model. This would imply that multiple models must be used/built for different particular problems. In the case of computational complexity, the “No Free Lunch” theorem states for all algorithms solving all problems that the average output would be […]