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	Ottimizzazione Bayesiana a Sciame di Particelle	Multi-Objective Particle Swarm Optimization (MOPSO)
Campo	Simulazione	Simulazione
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2003	2004
Ideatore	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Tipo	Hybrid metaheuristic — Bayesian probabilistic swarm search	Population-based swarm metaheuristic
Fonte seminale	Higashi, N., Iba, H. (2003). Particle swarm optimization with Gaussian mutation. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, pp. 72-79.	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.
Alias	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Correlati	6	5
Sintesi	Bayesian Particle Swarm Optimization (Bayesian PSO) integrates Bayesian probabilistic reasoning into the standard particle swarm framework. Particles update their velocities and positions guided not only by personal and global best positions but also by a Bayesian posterior that encodes prior knowledge about the solution space, enabling more directed and statistically principled exploration of complex optimization landscapes.	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.