concept

Bacterial Foraging Optimization Algorithm

Also known as: BFO, bacterial foraging optimization algorithm, Bacterial Foraging Optimization

Facts (17)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 16 facts

measurementBarolli et al. (2020) reported that using Grey Wolf Optimizer (GWO) and Bacterial Foraging Optimization (BFO) techniques in a Home Energy Management System (HEMS) resulted in 45% and 55% energy reductions, respectively.

referencePriya Esther et al. (2016) applied the bacterial foraging optimization algorithm to demand-side management.

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.

referencePriya Esther B et al. (2016) explored demand-side management using the bacterial foraging optimization algorithm, published in 'Information systems design and intelligent applications' by Springer.

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.

claimIn the context of energy management optimization, BFOA stands for Bacterial foraging optimization algorithm.

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.

measurementBarolli et al. (2020) reported that using Grey Wolf Optimizer (GWO) and Bacterial Foraging Optimization (BFO) techniques in Home Energy Management Systems (HEMS) resulted in 45% and 55% energy reductions, respectively.

referenceChen H, Wang L, Di J, and Ping S published a study on bacterial foraging optimization based on a self-adaptive chemotaxis strategy in Computational Intelligence and Neuroscience in 2020.

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, BFO stands for Bacterial foraging optimization.

referenceElmouatamid et al. (2020) evaluated the performance of a Home Energy Management System (HEMS) using three meta-heuristic optimization techniques: Harmony Search (HS), Bacterial Foraging Optimization (BFO), and EDE algorithms.

measurementPriya Esther et al. (2016) utilized the bacterial foraging optimization (BFO) algorithm to reduce peak load by 7% and energy expenditures by 10% for various consumer loads, outperforming earlier evolutionary algorithms.

claimElmouatamid et al. (2020) evaluated the performance of a Home Energy Management System (HEMS) using three meta-heuristic optimization techniques: Harmony Search (HS), Bacterial Foraging Optimization (BFO), and EDE algorithms.

measurementBacterial Foraging Optimization (BFO) was used to reduce peak load by 7% and energy expenditures by 10% for various consumer loads, outperforming earlier evolutionary algorithms for controlling devices (Priya Esther et al. 2016).

Construction of intelligent decision support systems through ... - Nature nature.com Nature Oct 10, 2025 1 fact

claimThe Core Ontology Tier of the Knowledge Representation Layer integrates established upper ontologies, specifically DOLCE and BFO, to ensure interoperability and create domain-independent concepts and relationships.