Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Pemodelan Berasaskan Agen Berbilang Objektif× | Algoritma Genetik Multi-Objektif (MOGA)× | |
|---|---|---|
| Bidang | Simulasi | Simulasi |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2001-2006 | 1984 |
| Pengasas≠ | Deb, K.; Tesfatsion, L. et al. | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Jenis≠ | Simulation-optimization hybrid | Population-based evolutionary optimizer |
| Sumber perintis≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396 | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Alias | MO-ABM, Multi-objective ABM, Pareto-based agent-based modeling, Multi-objective agent simulation | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Berkaitan | 4 | 4 |
| Ringkasan≠ | Multi-Objective Agent-Based Modeling (MO-ABM) couples agent-based simulation with multi-objective optimization to simultaneously optimize several conflicting performance criteria across complex adaptive systems. Autonomous agents interact according to behavioral rules while an optimizer searches for parameter configurations that achieve Pareto-optimal trade-offs among competing system-level goals. | 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. |
| ScholarGateSet data ↗ |
|
|