Process / pipelineSimulation / optimization

Policy Scenario Particle Swarm Optimization — PSO-driven search across alternative policy futures

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.

Open in MethodMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. DOI: 10.1109/ICNN.1995.488968
  2. Poli, R., Kennedy, J., Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm Intelligence, 1(1), 33–57. DOI: 10.1007/s11721-007-0002-0

Related methods

ScholarGatePolicy Scenario Particle Swarm Optimization (Policy Scenario Particle Swarm Optimization — PSO-driven search across alternative policy futures). Retrieved 2026-06-04 from https://scholargate.app/en/simulation/policy-scenario-particle-swarm-optimization