Discussing the article: "Artificial Bee Hive Algorithm (ABHA): Theory and methods"
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Check out the new article: Artificial Bee Hive Algorithm (ABHA): Theory and methods.
In this article, we will consider the Artificial Bee Hive Algorithm (ABHA) developed in 2009. The algorithm is aimed at solving continuous optimization problems. We will look at how ABHA draws inspiration from the behavior of a bee colony, where each bee has a unique role that helps them find resources more efficiently.
The new artificial beehive algorithm considered here provides a more comprehensive and in-depth look at bees' foraging behavior, demonstrating how collective interaction and role assignments facilitate the search for new food sources. It demonstrates how interactions between agents can lead to more efficient outcomes. The algorithm takes a closer look at the individual roles in a bee colony.
The main goal of ABHA is to find optimal solutions in high-dimensional spaces where functions may have many local minima and maxima. This makes the optimization problem particularly challenging, as traditional methods can get stuck at local extremes without reaching the global optimum. The ABHA algorithm draws inspiration from the efficient foraging strategies used by bees. In nature, bees use collective methods to efficiently find nectar sources, and this principle has been adapted to create an algorithm that can improve the process of finding optimal solutions.
The ABHA structure includes various states that reflect the dynamics of bee behavior. One such state is the "experimental state," during which bees exchange information about food sources they have found. This state promotes the accumulation of knowledge about the most productive areas of multidimensional space. Another important state is the "search state", when bees actively explore the space in search of the best sources, using information received from their brethren.
Author: Andrey Dik