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| Deterministic Particle Swarm Optimization× | Simulated Annealing× | |
|---|---|---|
| Fachgebiet≠ | Simulation | Optimierung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1995 (PSO); deterministic formulation circa 2002 | 1983 |
| Urheber≠ | Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature | — |
| Typ≠ | Swarm intelligence metaheuristic — deterministic variant | Probabilistic metaheuristic / local search |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen≠ | DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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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