Сравнение на методи

	Многокритериална оптимизация с алгоритъм на мравките (MOACO)	Оптимизация чрез мравчена колония
Област	Симулационно моделиране	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1999	1992 (foundational thesis); 1997 (Ant Colony System formalization)
Създател	Gambardella, Taillard & Agazzi; Dorigo & Stützle	—
Тип	Population-based metaheuristic	Metaheuristic — swarm intelligence
Основополагащ източник	Gambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In D. Corne, M. Dorigo, & F. Glover (Eds.), New Ideas in Optimization (pp. 63–76). McGraw-Hill.	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.
Други названия	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system
Свързани	4	5
Резюме	Multi-Objective Ant Colony Optimization (MOACO) is a swarm-intelligence metaheuristic that extends the classic Ant Colony Optimization framework to simultaneously optimize two or more conflicting objectives. Artificial ants construct candidate solutions guided by pheromone trails and heuristic information, progressively building an archive of Pareto-optimal solutions rather than converging to a single best answer.	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.