方法对比
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| 确定性粒子群优化× | 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≠ | 1995 (PSO); deterministic formulation circa 2002 | 2004 |
| 提出者≠ | Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature | Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. |
| 类型≠ | Swarm intelligence metaheuristic — deterministic variant | Population-based swarm metaheuristic |
| 开创性文献≠ | Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE. DOI ↗ | 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 | DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO | MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO |
| 相关≠ | 6 | 5 |
| 摘要≠ | Deterministic Particle Swarm Optimization (DPSO) removes the stochastic random coefficients from classical PSO, replacing them with fixed cognitive and social acceleration parameters. Particles move through the search space following fully predictable trajectories, enabling reproducible convergence analysis and guaranteed termination behavior in continuous and combinatorial optimization problems. | 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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