Discussing the article: "Artificial Ecosystem-based Optimization (AEO) algorithm"

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Check out the new article: Artificial Ecosystem-based Optimization (AEO) algorithm.

The article considers a metaheuristic Artificial Ecosystem-based Optimization (AEO) algorithm, which simulates interactions between ecosystem components by creating an initial population of solutions and applying adaptive update strategies, and describes in detail the stages of AEO operation, including the consumption and decomposition phases, as well as different agent behavior strategies. The article introduces the features and advantages of this algorithm.

The AEO algorithm is based on several key principles observed in nature. Just as ecosystems contain many species, each adapted to its own ecological niche, AEO uses a population of diverse solutions. In this context, each solution can be viewed as a separate "species" with unique characteristics and adaptive capabilities.

In nature, energy is transferred from one organism to another through food chains. In AEO this is modeled through the interaction of different types of "agents" - from "grass" to "omnivores". Here, information, similar to energy, is transferred between solutions, which helps improve the overall quality of the population. Ecosystems are characterized by both competition for resources and symbiotic relationships. The AEO algorithm reflects these processes through decision updating strategies, where agents can "compete" for better positions or "cooperate" by exchanging information.


Author: Andrey Dik

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