방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 에이전트 기반 유전 알고리즘× | Agent-Based Multi-Objective Optimization× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s | 1990s–2000s |
| 창시자≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. |
| 유형≠ | Hybrid evolutionary-agent simulation | Simulation-driven multi-objective search |
| 원전≠ | Adamidis, P., & Petridis, V. (1996). Co-operating populations with different evolution behaviors. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1996), 188-191. IEEE. link ↗ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 |
| 별칭 | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO |
| 관련 | 5 | 5 |
| 요약≠ | An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration of the search space while preserving population diversity and avoiding premature convergence. | 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. |
| ScholarGate데이터셋 ↗ |
|
|