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Otimização por Enxame de Partículas Multi-Objetivo (MOPSO)×Otimização Multi-Objetivo por Colônia de Formigas (MOACO)×
ÁreaSimulaçãoSimulação
FamíliaProcess / pipelineProcess / pipeline
Ano de origem20041999
Autor originalCoello Coello, C. A., Pulido, G. T., & Lechuga, M. S.Gambardella, Taillard & Agazzi; Dorigo & Stützle
TipoPopulation-based swarm metaheuristicPopulation-based metaheuristic
Fonte seminalCoello 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 ↗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. link ↗
Outros nomesMOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSOMOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO
Relacionados54
ResumoMulti-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.
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ScholarGateComparar métodos: Multi-objective particle swarm optimization · Multi-objective ant colony optimization. Recuperado em 2026-06-17 de https://scholargate.app/pt/compare