Algoritmo Colônia Artificial de Abelhas: um estudo comparativo

Abstract

Optimizing complex problems is a constant challenge in many fields. Inspired by the intelligent behavior of bees in the food foraging process, the Artificial Bee Colony (ABC) algorithm has emerged as a promising tool for solving optimization problems and has gained significant attention among researchers. In this study, the performance of ABC was compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm on a set of 12 benchmark functions. The results indicate that ABC exhibits competitive, and in some cases superior, performance compared to traditional algorithms. Its robustness and efficiency, coupled with a smaller number of parameters, make it a promising tool for solving multimodal and multidimensional optimization problems.