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| Stochastischer Genetischer Algorithmus× | Stochastische Partikelschwarmoptimierung× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1975 | 1995–2002 |
| Urheber≠ | Holland, J. H. | Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community |
| Typ≠ | Stochastic evolutionary metaheuristic | Metaheuristic optimization — stochastic swarm intelligence |
| Wegweisende Quelle≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 | Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE. DOI ↗ |
| Aliasnamen | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm | Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research. | Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity throughout the search. It is widely applied to continuous, mixed, and noisy optimization problems in engineering, operations research, and simulation-based design. |
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