Discussing the article: "The Dragonfly Algorithm (DA)"

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Check out the new article: The Dragonfly Algorithm (DA).

In this article, we will examine the Dragonfly Algorithm (DA), inspired by the collective behavior of dragonflies in nature — their ability to coordinate flight in a swarm, avoid collisions, follow prey, and evade predators. Let's look at how five simple behavioral rules and an adaptive mechanism for transitioning from exploration to exploitation are implemented in MQL5, and test the algorithm on our test bench.

Let us consider another modern metaheuristic population-based method, the Dragonfly Algorithm, for our optimization tasks. Dragonflies are among the most spectacular flying insects on the planet and have existed for nearly 300 million years. Their unique ability to maneuver in the air—hovering, instantaneous changes in direction, and flight in six degrees of freedom—has long attracted the attention of researchers. However, in the field of metaheuristic optimization, it is not the aerodynamics of an individual dragonfly that is of particular interest, but rather the collective behavior of swarms.

In 2015, Seyedali Mirjalili proposed the Dragonfly Algorithm (DA), which models two types of dragonfly swarm behavior: static and dynamic. A static swarm forms during hunting—dragonflies gather in small groups and move over a limited area in search of prey. A dynamic swarm forms during migration—large groups move in the same direction over considerable distances. These two modes naturally correspond to the two fundamental phases of optimization: exploitation (exploiting found solutions) and exploration (exploring new search space).

Author: Andrey Dik