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| 다목적 개미 군집 최적화 (MOACO)× | 다목적 입자 군집 최적화 (MOPSO)× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1999 | 2004 |
| 창시자≠ | Gambardella, Taillard & Agazzi; Dorigo & Stützle | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. |
| 유형≠ | Population-based metaheuristic | Population-based swarm metaheuristic |
| 원전≠ | 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 ↗ | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279. DOI ↗ |
| 별칭 | MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO | MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information. |
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