方法对比
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| 确定性遗传算法× | 确定性粒子群优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1975–1989 | 1995 (PSO); deterministic formulation circa 2002 |
| 提出者≠ | Goldberg, D. E.; Holland, J. H. | Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature |
| 类型≠ | Deterministic evolutionary optimization | Swarm intelligence metaheuristic — deterministic variant |
| 开创性文献≠ | Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673 | Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE. DOI ↗ |
| 别名 | DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA | DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO |
| 相关≠ | 5 | 6 |
| 摘要≠ | A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable. | 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. |
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