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| 에이전트 기반 개미 군집 최적화× | 다목적 개미 군집 최적화 (MOACO)× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1992-2004 | 1999 |
| 창시자≠ | Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community | Gambardella, Taillard & Agazzi; Dorigo & Stützle |
| 유형≠ | Metaheuristic optimization — agent-based swarm simulation | Population-based metaheuristic |
| 원전≠ | Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA. ISBN: 9780262042192 | Gambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In D. Corne, M. Dorigo, & F. Glover (Eds.), New Ideas in Optimization (pp. 63–76). McGraw-Hill. link ↗ |
| 별칭 | AB-ACO, Agent-Based ACO, Multi-Agent Ant Colony Optimization, MAACO | MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO |
| 관련≠ | 5 | 4 |
| 요약≠ | Agent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on a search graph. By coupling agent-level behavioral rules with a shared pheromone environment, the collective system converges on high-quality solutions to hard combinatorial and simulation-embedded optimization problems without central coordination. | Multi-Objective Ant Colony Optimization (MOACO) is a swarm-intelligence metaheuristic that extends the classic Ant Colony Optimization framework to simultaneously optimize two or more conflicting objectives. Artificial ants construct candidate solutions guided by pheromone trails and heuristic information, progressively building an archive of Pareto-optimal solutions rather than converging to a single best answer. |
| ScholarGate데이터셋 ↗ |
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