方法对比

	鲁棒粒子群优化	鲁棒模拟退火
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	2000s	1983 (SA); robust variant emerged 1990s–2000s
提出者	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s	Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community
类型	Metaheuristic — robust swarm-based optimizer	Metaheuristic with robustness evaluation
开创性文献	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954	Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
别名	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness	RSA, Robust SA, Uncertainty-robust simulated annealing, Worst-case simulated annealing
相关	6	5
摘要	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.	Robust Simulated Annealing (RSA) adapts the classical simulated annealing metaheuristic to seek solutions that perform well not just under nominal conditions but across the full range of uncertain or adversarial parameter values. By embedding a robustness evaluation — worst-case, expected-case, or regret-based — into the SA acceptance step, RSA trades some nominal optimality for resilience, making it valuable when problem parameters are imprecisely known or subject to environmental variation.