Discussing the article: "Dandelion Optimizer (DO)"

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Check out the new article: Dandelion Optimizer (DO).

The Dandelion Optimizer (DO) turns the simple flight of a seed carried by the wind into a mathematical search strategy. The three phases — vortex rising, drift toward the center of the population, and landing along a Lévy-flight trajectory — form an elegant metaphor that yields interesting results in practice.

We have all seen how the wind picks up fluffy dandelion seeds and scatters them far and wide. This simple natural process turned out to be an interesting search strategy that researchers transformed into an optimization algorithm.

Imagine a clearing where a dandelion is growing. Its goal is to find the best places for its seeds to sprout. Seeds cannot choose their own direction, but wind, air turbulence, and the unique shape of the fluffy parachute (pappus) create complex flight paths. As a result, the seeds are spread over a wide area, and some of them find ideal conditions for growth.

The DO algorithm simulates this process. Each “seed” is a potential solution to an optimization problem. The coordinates of a seed in space are the values of the parameters to be optimized. “Soil quality” at the landing point is the value of the objective function.

Three phases of seed flight. The flight of a dandelion seed is divided into three consecutive phases, each of which plays a role in finding the optimal solution.


Author: Andrey Dik

 
Hello, author. Could you please write a simple example of an Expert Advisor (EA) that uses your optimisation algorithm?
 
hini a simple example of an Expert Advisor (EA) that uses your optimisation algorithm?

Hello.

Of course, here you go:

Articles

An advisor based on a universal MLP approximator

Andrey Dik, 13 December 2024 13:09

This article presents a simple and accessible way to use a neural network in a trading advisor, which does not require in-depth knowledge of machine learning. The method avoids normalising the objective function and eliminates the problems of ‘weight explosion’ and ‘network stagnation’, offering intuitive training and clear monitoring of results.

Articles

Using optimisation algorithms to adjust an expert advisor’s parameters ‘on the fly’

Andrey Dik, 16 February 2024, 10:33

This article examines the practical aspects of using optimisation algorithms to find the best parameters for Expert Advisors ‘on the fly’, as well as the virtualisation of trading operations and Expert Advisor logic. This article can be used as a guide for implementing optimisation algorithms in a trading expert advisor.
Both articles use optimisation algorithms, accompanied by simple examples and source code.

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