Compara mètodes

	NSGA-II	Optimització de Colònies d'Escultures	Algorisme genètic
Camp	Optimització	Optimització	Optimització
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	2002	1992 (foundational thesis); 1997 (Ant Colony System formalization)	1975
Autor original	—	—	John Henry Holland
Tipus	Evolutionary multi-objective optimisation algorithm	Metaheuristic — swarm intelligence	Population-based metaheuristic
Font seminal	Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.	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.
Àlies	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Relacionats	4	5	5
Resum	NSGA-II (Non-dominated Sorting Genetic Algorithm II) is the standard reference algorithm for multi-objective evolutionary optimisation, introduced by Deb, Pratap, Agarwal and Meyarivan in 2002. Rather than collapsing multiple conflicting objectives into a single score, it evolves a population of candidate solutions across generations and returns a set of Pareto-optimal trade-off solutions — the Pareto front — using fast non-dominated sorting and a crowding distance metric to preserve diversity.	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.