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	Multi-Objective Particle Swarm Optimization (MOPSO)	Multi-Objective Ant Colony Optimization (MOACO)
Ämnesområde	Simulering	Simulering
Familj	Process / pipeline	Process / pipeline
Ursprungsår	2004	1999
Upphovsperson	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.	Gambardella, Taillard & Agazzi; Dorigo & Stützle
Typ	Population-based swarm metaheuristic	Population-based metaheuristic
Ursprungskälla	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.	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.
Alias	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO
Närliggande	5	4
Sammanfattning	Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.	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.