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| Agent-based multi-objective optimization× | Multi-Objective Particle Swarm Optimization (MOPSO)× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1990s–2000s | 2004 |
| Urheber≠ | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. |
| Typ≠ | Simulation-driven multi-objective search | Population-based swarm metaheuristic |
| Wegweisende Quelle≠ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 | 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 ↗ |
| Aliasnamen | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO | MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adaptive systems where objectives emerge from micro-level interactions rather than closed-form equations. | 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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