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| Multi-Objective Particle Swarm Optimization (MOPSO)× | Multi-Objective Genetic Algorithm (MOGA)× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2004 | 1984 |
| Urheber≠ | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Typ≠ | Population-based swarm metaheuristic | Population-based evolutionary optimizer |
| Wegweisende Quelle≠ | 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 ↗ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Aliasnamen | MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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. | A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among. |
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