Discussing the article: "Neuroboids Optimization Algorithm 2 (NOA2)"

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Check out the new article: Neuroboids Optimization Algorithm 2 (NOA2).

The new proprietary optimization algorithm NOA2 (Neuroboids Optimization Algorithm 2) combines the principles of swarm intelligence with neural control. NOA2 combines the mechanics of a neuroboid swarm with an adaptive neural system that allows agents to self-correct their behavior while searching for the optimum. The algorithm is under active development and demonstrates potential for solving complex optimization problems.

As I have already mentioned, the main idea of the neuroboids algorithm is to combine two paradigms: the collective intelligence of swarm algorithms and the adaptive learning of neural networks.

In the traditional boids algorithm, proposed by Craig Reynolds, agents follow three simple rules: convergence (moving toward the center of the group), separation (avoiding collisions), and alignment (matching speed with neighbors). These rules create realistic group behavior, similar to the behavior of birds in flocks. Neuroboids extend this concept by equipping each agent with an individual neural network that learns from the agent's experience exploring the search space. This neural network performs two key functions:

1. Adaptive motion control adjusts the agent's speed based on its current state and movement history.
2. Modification of the standard rules of boids dynamically adjusts the influence of convergence, separation, and alignment rules depending on the context.

The result is a hybrid algorithm where each agent retains the social behavior necessary for efficient exploration of the space, but at the same time individually adapts to the fitness function landscape through learning. This creates a self-regulating balance between exploration and exploitation.

The key advantages of this approach are the independent learning of agents in optimal movement strategies, as a result of which the algorithm automatically adapts to different types of optimization landscapes, while preserving the exploration of space, thanks to collective behavior without centralized control. Let me give you a simple analogy: imagine a flock of birds flying in the sky. They move in a coordinated manner: no one collides, they stick together and fly in the same direction. This behavior can be described by three simple rules: stay close to your neighbors (do not break away from the flock), do not collide with your neighbors (keep your distance), and fly in the same direction (maintain a common course).

Author: Andrey Dik