Discussing the article: "Animal Migration Optimization (AMO) algorithm"

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Check out the new article: Animal Migration Optimization (AMO) algorithm.

The article is devoted to the AMO algorithm, which models the seasonal migration of animals in search of optimal conditions for life and reproduction. The main features of AMO include the use of topological neighborhood and a probabilistic update mechanism, which makes it easy to implement and flexible for various optimization tasks.

The AMO algorithm simulates three main components of animal movement over long distances: avoiding collisions with neighboring individuals, moving in the same direction as the flock (group), and maintaining sufficient distance between each other. These principles not only help avoid conflicts, but also maintain collective behavior, which is critical for survival in the wild.

Optimization stages in the AMO algorithm. The algorithm includes two key stages of optimization in one iteration:

• Migration: During this stage, the position of the individual is updated taking into account its neighbors.
  
• Population renewal: at this stage, individuals are partially replaced by new ones, with a probability depending on the position of the individual in the flock.

Modeling the collective behavior of migratory animals can be an effective approach to solving complex optimization problems. The algorithm tries to balance exploration of the search space and exploitation of the best solutions found, mimicking natural processes.

Author: Andrey Dik