Сравнение методов

	Робастная оптимизация методами роя частиц	Многокритериальная оптимизация роем частиц (MOPSO)
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s	2004
Автор метода	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Тип	Metaheuristic — robust swarm-based optimizer	Population-based swarm metaheuristic
Основополагающий источник	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.
Другие названия	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Связанные	6	5
Сводка	Robust Particle Swarm Optimization (Robust PSO) extends the classical PSO metaheuristic to explicitly account for uncertainty in the objective function, constraints, or decision variables. Rather than optimizing a single nominal objective, each candidate solution is evaluated over a set of uncertainty scenarios, and fitness is judged by a robustness criterion such as worst-case performance or expected value, yielding solutions that remain near-optimal even when conditions deviate from nominal assumptions.	Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.