Discussing the article: "Artificial Cooperative Search (ACS) algorithm"

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Check out the new article: Artificial Cooperative Search (ACS) algorithm.

Artificial Cooperative Search (ACS) is an innovative method using a binary matrix and multiple dynamic populations based on mutualistic relationships and cooperation to find optimal solutions quickly and accurately. ACS unique approach to predators and prey enables it to achieve excellent results in numerical optimization problems.

The ACS algorithm was proposed by Pinar Civicioglu in 2013. It starts by using two base populations containing candidate solutions within the confidence region. The algorithm then creates two new populations, predators and prey, by mapping values from the initial α and β populations using random steps and a binary matrix. In addition, the fifth population is calculated based on the values in the predator and prey populations. The process involves updating the initial populations for a specified number of iterations, with the best solution being taken from these two populations.


The migration and evolution of two artificial superorganisms that interact biologically with each other to reach the global extremum of the objective function, and the process similar to the cooperative behavior in nature, are the key to the high performance of ACS in numerical optimization problems. This unique approach to biologically motivated interactions between populations allows the ACS algorithm to achieve impressive convergence speed and high solution accuracy. Due to its ability to quickly and accurately find optimal solutions, ACS has proven itself to be a powerful tool for solving a wide range of numerical optimization problems.

Author: Andrey Dik

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