Discussing the article: "Artificial Atom Algorithm (A3)"

[MetaQuotes]

Check out the new article: Artificial Atom Algorithm (A3).

The article describes implementation of the A3 algorithm - a metaheuristic optimization method inspired by chemical processes - in MQL5. Only two adjustable parameters, compactness and a small population, ensure high operating speed with sufficient quality of solutions.

The algorithm models the interaction of atoms and electrons to find the optimal solution. The basic components are atoms that represent potential solutions to the problem. Electrons represent decision variables. Covalent bonding is an operator for storing and replicating the best solutions, ionic bonding is an operator for exploring the search space and achieving a global optimum. 

The algorithm starts by randomly generating a set of atoms, then evaluates the quality of each atom using the objective function, and then applies covalent and ionic bond operators to improve solutions (unfortunately, there is no description of how this is done). Next, it is necessary to evaluate the effect of electrons. Again, it is unclear how to do that. Electrons and atoms should be sorted. Okay, we can sort the atoms (solutions), but how we can sort the electrons is a mystery (it is like trying to sort a person's limbs, arms with legs, right with left - it does not matter). Repeat the process iteratively until the stopping criterion is reached. Well, as the saying goes, you have to work with what you have. We need to try to assemble an algorithm applying the authors' ideas.

Author: Andrey Dik

[ماريو عطيه]

طاقة كوشي :

اطلع على المقال الجديد: الخوارزمية التحليلية الاصطناعية (A3) .

المؤلف: أندريه ديك

I didn't understand anything

[Vinicius Pereira De Oliveira]

ماريو عطيه #:  I didn’t understand a thing

Short, to the point and brutally honest! 😂

[Auvoria Prime]

same here, first read was not easy lol. the idea sounds interesting, but when the rules for covalent/ionic bonding and electron sorting are not very clear, its hard to judge the algorithm properly.

for me the useful part would be a simple step by step example with maybe 2 atoms and a few variables, then show exactly how one iteration changes the solution. without that, it feels more like a concept than something traders can really understand or test.

[Chacha Ian Maroa]

Such a crazy concept. Please write about the Big Bang theory optimization concept next

[Ryan L Johnson]

Chacha Ian Maroa #:
Such a crazy concept. Please write about the Big Bang theory optimization concept next

Dude had enough of Roscosmos, and landed on mql5.com instead.

[Robson Bruno Teixeira De Sousa]

My Review

Imagine a group of 100 children looking for a toy hidden in a park.

Each child starts in a different place.

The algorithm works as follows:

1. In the first round, it scatters all the children around the park in random locations.
2. Then, in each round:
   • Some children look to see who is closest to the toy and walk a little way in that direction.
   • Other children follow a child chosen at random from among those who are doing well.
   • Every now and then, a child decides to explore a completely new area, to see if they can find a better route.

3. Whenever a child tries to leave the park, the algorithm says:

   “No! Stay within the boundaries of the park.”

4. This process is repeated several times.

In the end, as they all learn from those who are finding better paths, the whole group gets closer and closer to the toy.

In a nutshell:

The algorithm gets several ‘people’ to try to solve a problem, learning from the best solutions and, sometimes, exploring new places to find an even better solution.

Note: The code is still at an intermediate stage and needs some tweaking.

[Andrey Dik]

Chacha Ian Maroa #:
What a crazy idea. Please write about the concept of optimising the Big Bang theory next time

;D

https://www.mql5.com/en/articles/16963