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| Stochastic Particle Swarm Optimization× | Stokastisk Multi-Objektiv Optimering× | |
|---|---|---|
| Fagområde | Simulering | Simulering |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1995–2002 | 1990s–2000s |
| Ophavsperson≠ | Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| Type≠ | Metaheuristic optimization — stochastic swarm intelligence | Stochastic metaheuristic optimization |
| Oprindelig kilde≠ | Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE. DOI ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Aliasser | Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| Relaterede≠ | 4 | 5 |
| Resumé≠ | 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. | Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty. |
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