Bandingkan metode

	Policy Scenario Particle Swarm Optimization	Optimasi Partikel Koloni yang Kuat
Bidang	Simulasi	Simulasi
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1995 (PSO); applied to policy scenarios from 2000s onward	2000s
Pencetus	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s
Tipe	Metaheuristic optimization within policy scenario framework	Metaheuristic — robust swarm-based optimizer
Sumber perintis	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954
Alias	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness
Terkait	6	6
Ringkasan	Policy Scenario Particle Swarm Optimization integrates Particle Swarm Optimization (PSO) with explicit policy scenario analysis. A swarm of candidate policy solutions is evaluated under multiple defined future scenarios, and PSO's velocity-position update rules guide the swarm toward solutions that perform well—or robustly—across all considered scenarios. It is used in energy, environmental, infrastructure, and public resource planning.	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.