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Multi-Objective Particle Swarm Optimization (MOPSO)×Ricottura Simulata Multi-Obiettivo (MOSA)×
CampoSimulazioneSimulazione
FamigliaProcess / pipelineProcess / pipeline
Anno di origine20041992–1998
IdeatoreCoello Coello, C. A., Pulido, G. T., & Lechuga, M. S.Serafini, P.; Czyzak, P. and Jaszkiewicz, A.
TipoPopulation-based swarm metaheuristicMetaheuristic / Pareto-based optimizer
Fonte seminaleCoello 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 ↗Czyzak, P., Jaszkiewicz, A. (1998). Pareto simulated annealing — a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7(1), 34–47. DOI ↗
AliasMOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSOMOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA
Correlati55
SintesiMulti-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 Simulated Annealing (MOSA) extends the classical simulated annealing metaheuristic to problems with two or more conflicting objective functions. Instead of converging to a single optimum, MOSA explores the solution space stochastically and maintains an archive of non-dominated (Pareto-optimal) solutions, offering decision-makers a diverse trade-off front rather than one prescribed answer.
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ScholarGateConfronta i metodi: Multi-objective particle swarm optimization · Multi-objective simulated annealing. Consultato il 2026-06-17 da https://scholargate.app/it/compare