Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Simulated Annealing Multi-Obiectiv (MOSA)× | Multi-Objective Particle Swarm Optimization (MOPSO)× | |
|---|---|---|
| Domeniu | Simulare | Simulare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1992–1998 | 2004 |
| Autorul original≠ | Serafini, P.; Czyzak, P. and Jaszkiewicz, A. | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. |
| Tip≠ | Metaheuristic / Pareto-based optimizer | Population-based swarm metaheuristic |
| Sursa seminală≠ | 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 ↗ | 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 ↗ |
| Denumiri alternative | MOSA, Multi-Criteria Simulated Annealing, Pareto Simulated Annealing, PSA | MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO |
| Înrudite | 5 | 5 |
| Rezumat≠ | 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. | 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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