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Optimización Anticolectiva Multi-Objetivo (MOACO)×Multi-Objective Particle Swarm Optimization (MOPSO)×
CampoSimulaciónSimulación
FamiliaProcess / pipelineProcess / pipeline
Año de origen19992004
Autor originalGambardella, Taillard & Agazzi; Dorigo & StützleCoello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
TipoPopulation-based metaheuristicPopulation-based swarm metaheuristic
Fuente seminalGambardella, 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. link ↗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. DOI ↗
AliasMOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACOMOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Relacionados45
ResumenMulti-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.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.
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ScholarGateComparar métodos: Multi-objective ant colony optimization · Multi-objective particle swarm optimization. Recuperado el 2026-06-17 de https://scholargate.app/es/compare