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	Multi-objective Tabu Search (MOTS)	Wieloobiektywowa optymalizacja kolonii mrówek (MOACO)
Dziedzina	Symulacja	Symulacja
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1997	1999
Twórca	Hansen, M. P.; building on Glover (1989) Tabu Search	Gambardella, Taillard & Agazzi; Dorigo & Stützle
Typ	Metaheuristic multi-objective optimization	Population-based metaheuristic
Źródło pierwotne	Hansen, M. P. (1997). Tabu search for multiobjective optimization: MOTS. Presented at the 13th International Conference on Multiple Criteria Decision Making (MCDM), Cape Town, South Africa.	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.
Inne nazwy	MOTS, Multi-criteria Tabu Search, Pareto Tabu Search, TSMOO	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO
Pokrewne	5	4
Podsumowanie	Multi-objective Tabu Search (MOTS) is a metaheuristic algorithm that extends the classic Tabu Search framework to simultaneously optimize two or more conflicting objective functions. Instead of a single optimum, it seeks to approximate the Pareto front — the set of solutions where no objective can be improved without worsening another — making it suitable for complex combinatorial and continuous optimization problems in engineering, logistics, and operations research.	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.