방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Agent-Based Multi-Objective Optimization× | 다목적 유전 알고리즘 (MOGA)× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s–2000s | 1984 |
| 창시자≠ | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| 유형≠ | Simulation-driven multi-objective search | Population-based evolutionary optimizer |
| 원전≠ | 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 |
| 별칭 | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|