Σύγκριση μεθόδων

	Γενετικός Αλγόριθμος	Βελτιστοποίηση Βάσει Σμήνους Μυρμηγκιών	NSGA-II
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	1975	1992 (foundational thesis); 1997 (Ant Colony System formalization)	2002
Δημιουργός	John Henry Holland	—	—
Τύπος	Population-based metaheuristic	Metaheuristic — swarm intelligence	Evolutionary multi-objective optimisation algorithm
Θεμελιώδης πηγή	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	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.	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.
Εναλλακτικές ονομασίες	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system	NSGA2, Non-dominated Sorting GA II, NSGA-II — Çok Amaçlı Evrimsel Optimizasyon
Συναφείς	5	5	4
Σύνοψη	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.	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.	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.