Bandingkan metode

	Policy Scenario Particle Swarm Optimization	Optimasi Kawanan Partikel Multi-Objektif (MOPSO)
Bidang	Simulasi	Simulasi
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1995 (PSO); applied to policy scenarios from 2000s onward	2004
Pencetus	Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature	Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.
Tipe	Metaheuristic optimization within policy scenario framework	Population-based swarm metaheuristic
Sumber perintis	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.	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.
Alias	PS-PSO, Policy PSO, Scenario-based PSO, Policy scenario swarm optimization	MOPSO, Multi-objective PSO, Pareto PSO, Vector-evaluated PSO
Terkait	6	5
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.	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.