Discussing the article: "Neuroboids Optimization Algorithm (NOA)"

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Check out the new article: Neuroboids Optimization Algorithm (NOA).

A new bioinspired optimization metaheuristic, NOA (Neuroboids Optimization Algorithm), combines the principles of collective intelligence and neural networks. Unlike conventional methods, the algorithm uses a population of self-learning "neuroboids", each with its own neural network that adapts its search strategy in real time. The article reveals the architecture of the algorithm, the mechanisms of self-learning of agents, and the prospects for applying this hybrid approach to complex optimization problems.

Imagine that you are walking in the garden after the rain. Earthworms are everywhere - simple creatures with a primitive nervous system. They do not have the ability to "think" in our sense, but somehow they find their way through difficult terrain, avoid danger, find food and partners. Their tiny brains contain only a few thousand neurons, yet they have existed for millions of years. This is how the idea of neuroboids was born.

What if we combined the simplicity of a worm with the power of collective intelligence? In nature, simple organisms achieve incredible results when they work together — ants build complex colonies, bees solve optimization problems when collecting nectar, and flocks of birds form complex dynamic structures without centralized control.

My neuroboids are like these earthworms. Each one has its own small neural network - not some massive architecture with millions of parameters, but just a few neurons at the input and output. They do not know the entire search space, they only see their local environment. When one worm finds a fertile patch of soil rich in nutrients, others gradually gravitate towards that spot. But they do not just follow blindly – each one maintains their own individuality, their own movement strategy. The neuroboids do not need to know all the math behind optimization. They learn on their own, through trial and error. When one of them finds a good solution, the others do not just copy its coordinates, but learn to understand why this solution is good and how to get there on their own.

Author: Andrey Dik