Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Aģentu balstīta daudzobjektu optimizācija× | Daudzobjektīvu ģenētisks algoritms (MOGA)× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1990s–2000s | 1984 |
| Autors≠ | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| Tips≠ | Simulation-driven multi-objective search | Population-based evolutionary optimizer |
| Pirmavots≠ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| Citi nosaukumi | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | 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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