方法对比
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| 多目标禁忌搜索 (MOTS)× | 多目标粒子群优化 (MOPSO)× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1997 | 2004 |
| 提出者≠ | Hansen, M. P.; building on Glover (1989) Tabu Search | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. |
| 类型≠ | Metaheuristic multi-objective optimization | Population-based swarm metaheuristic |
| 开创性文献≠ | Hansen, M. P. (1997). Tabu search for multiobjective optimization: MOTS. Presented at the 13th International Conference on Multiple Criteria Decision Making (MCDM), Cape Town, South Africa. 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 ↗ |
| 别名 | MOTS, Multi-criteria Tabu Search, Pareto Tabu Search, TSMOO | MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO |
| 相关 | 5 | 5 |
| 摘要≠ | Multi-objective Tabu Search (MOTS) is a metaheuristic algorithm that extends the classic Tabu Search framework to simultaneously optimize two or more conflicting objective functions. Instead of a single optimum, it seeks to approximate the Pareto front — the set of solutions where no objective can be improved without worsening another — making it suitable for complex combinatorial and continuous optimization problems in engineering, logistics, and operations research. | 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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