Linganisha mbinu

	Uendeshaji wa Msako wa Msako unaotegemea Ajaneti	Uboreshaji wa Koloni la Sisimizi Lenye Malengo Mengi (MOACO)
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1992-2004	1999
Mwanzilishi	Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community	Gambardella, Taillard & Agazzi; Dorigo & Stützle
Aina	Metaheuristic optimization — agent-based swarm simulation	Population-based metaheuristic
Chanzo asilia	Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA. ISBN: 9780262042192	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.
Majina mbadala	AB-ACO, Agent-Based ACO, Multi-Agent Ant Colony Optimization, MAACO	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO
Zinazohusiana	5	4
Muhtasari	Agent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on a search graph. By coupling agent-level behavioral rules with a shared pheromone environment, the collective system converges on high-quality solutions to hard combinatorial and simulation-embedded optimization problems without central coordination.	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.