方法对比
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| 确定性粒子群优化× | 模拟退火× | |
|---|---|---|
| 领域≠ | 仿真 | 优化 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1995 (PSO); deterministic formulation circa 2002 | 1983 |
| 提出者≠ | Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature | — |
| 类型≠ | Swarm intelligence metaheuristic — deterministic variant | Probabilistic metaheuristic / local search |
| 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 ↗ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| 别名≠ | DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| 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. | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. |
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