concept

binary particle swarm optimization

Also known as: BPSO

Facts (11)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 11 facts

measurementAhmad et al. (2017) reported that the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Bacterial Foraging Optimization (BFO), and Wind-Driven Optimization (WDO) algorithms achieved Peak-to-Average Ratio (PAR) reductions of 14.09%, 3.30%, 22.10%, and 33.54% respectively.

claimRahim et al. (2016b) proposed an energy management effort using Binary Particle Swarm Optimization (BPSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) to lower power prices and the peak-to-average ratio (PAR) while incorporating renewable energy sources and storage systems.

measurementAhmad et al. (2017) reported that the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Bacterial Foraging Optimization (BFO), and Wind-Driven Optimization (WDO) algorithms achieved power bill decreases of 9.80%, 19.50%, 15.40%, and 15.80% respectively.

measurementIn a study by Ahmad et al. (2017), the percentage of power bill decrease for GA, BPSO, BFO, and WDO algorithms was 9.80%, 19.50%, 15.40%, and 15.80% respectively.

measurementIn a study by Ahmad et al. (2017), the percentage of peak-to-average ratio (PAR) reduction for GA, BPSO, BFO, and WDO algorithms was 14.09%, 3.30%, 22.10%, and 33.54% respectively.

measurementJavaid et al. (2017a) found that the GAPSO algorithm outperformed GA and BPSO in cost and discomfort metrics, reducing peak power use by 27.7794% and peak-to-average ratio (PAR) by 36.39%, while reducing energy consumption costs by 25.2923%.

measurementJavaid et al. (2017a) reported that GA and BPSO reduced energy consumption costs by 24.0470% and 29.9702% respectively.

referenceRahim et al. (2016b) proposed an energy management effort using binary particle swarm optimization (BPSO), ant colony optimization (ACO), and genetic algorithm (GA) to lower power prices and the peak-to-average ratio (PAR) while accounting for renewable energy sources (RESs) and storage systems.

claimAhmad et al. (2017) introduced the hybrid GA/PSO method (HGPSO), which outperformed the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Bacterial Foraging Optimization (BFO), and Wind-Driven Optimization (WDO) algorithms.

claimIn the context of energy management optimization, BPSO stands for Binary particle swarm optimization.

measurementJavaid et al. (2017a) found that the GAPSO algorithm outperformed GA and BPSO in cost and discomfort metrics, reducing peak power consumption by 36.39% and energy consumption costs by up to 25.2923%, while requiring the least waiting time.