Discussing the article: "Atmosphere Clouds Model Optimization (ACMO): Theory"
Fascinating. Thanks a lot. Do you have some references for this algorithm?
Very genius, is this better than Binary Genetic Algorithm mate?
Gigantum Investment genetic algorithm?
It's hard to say. Every algorithm is good in its own way, it depends on the task. ;)
Great work , Andrew!
Andrey Dik #:
It's hard to say. Every algorithm is good in its own way, it depends on the task. ;)
yeah, you are the one and only i know the most genius Russian dev and you was make a compare between all of algorithm, the result for BGA is around 76 which is very high and top of all algorithm. But i was ask gpt for that, BGA is for decision making and ACMO is for continous learning. Am i correct mate?
It's hard to say. Every algorithm is good in its own way, it depends on the task. ;)
Hi Andrew, just an idea to improve the code. Can you use the Kowailk Function ? I put the article in attachment, they talk about it. Greetings
Files:
JOC24-3-4.zip
328 kb
Also, I want to know how you will replace Humidity and Air pression values; which criteria do you will select ?
Gigantum Investment #:
...
But I was asking gpt that BGA is for decision making and ACMO is for continuous learning. Am I right, mate?
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Check out the new article: Atmosphere Clouds Model Optimization (ACMO): Theory.
The article is devoted to the metaheuristic Atmosphere Clouds Model Optimization (ACMO) algorithm, which simulates the behavior of clouds to solve optimization problems. The algorithm uses the principles of cloud generation, movement and propagation, adapting to the "weather conditions" in the solution space. The article reveals how the algorithm's meteorological simulation finds optimal solutions in a complex possibility space and describes in detail the stages of ACMO operation, including "sky" preparation, cloud birth, cloud movement, and rain concentration.
Imagine a vast virtual sky where clouds form and move just like in the real atmosphere. The weather here is not just a set of conditions, but a living system in which humidity and atmospheric pressure influence every decision. Inspired by natural phenomena, the ACMO algorithm uses principles of cloud formation to explore the solution space, similar to how clouds form, spread, and disappear in the sky, trying to find optimal paths. The algorithm was proposed by Yan et al. and published in 2013.
Here we will look at each step of the ACMO algorithm in detail, starting with preparing the "sky" where clouds are born as potential solutions. We will follow their movements through virtual celestial space, observing how they adapt and change depending on weather conditions. As you delve into this fascinating process, you will see how clouds, like research teams, strive to find optimal solutions in a maze of possibilities. Let's uncover the secrets of this algorithm together and understand how it works, step by step.
Author: Andrey Dik