Comparar métodos

	Optimización por Colonia de Abejas Artificiales (ABC)	Optimización por Colonia de Hormigas	Algoritmo Genético
Campo	Optimización	Optimización	Optimización
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Año de origen	2007	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1975
Autor original	Dervis Karaboga & Bahriye Basturk	—	John Henry Holland
Tipo	Swarm Intelligence Metaheuristic	Metaheuristic — swarm intelligence	Population-based metaheuristic
Fuente seminal	Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.	Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Alias	ABC Algorithm, Bee Colony Optimization, Swarm-Based Bee Search, Yapay Arı Kolonisi	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Relacionados	3	5	5
Resumen	Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among three bee types — employed, onlooker, and scout — and iteratively refines them through local search and probabilistic selection, making it well-suited for researchers and engineers tackling complex, multimodal optimization landscapes.	Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.