Comparar métodos

	Optimización por Colonia de Hormigas	Algoritmo Genético	Optimización por Enjambre de Partículas (PSO)
Campo	Optimización	Optimización	Optimización
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Año de origen	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1975	1995
Autor original	—	John Henry Holland	—
Tipo	Metaheuristic — swarm intelligence	Population-based metaheuristic	Population-based metaheuristic / swarm intelligence
Fuente seminal	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relacionados	5	5	6
Resumen	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.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.