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wind-driven optimization

Also known as: WDO

Facts (9)

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A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 9 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.
measurementThe hybrid genetic wind-driven (HGWD) algorithm outperformed the Wind Driven Optimization (WDO) algorithm and the Genetic Algorithm (GA) by lowering power usage costs by 33% and 10% respectively.
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.
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.
measurementJavaid et al. (2017b) reported that smart home (SH) expenses were decreased by the Wind-Driven Optimization (WDO), Harmony Search Algorithm (HSA), Genetic Algorithm (GA), and Genetic Harmony Search Algorithm (GHSA) to 2.61, 1.72, 1.12, and 1.34 cents/h, respectively.
measurementJavaid et al. (2017b) found that smart home (SH) expenses were decreased by WDO, HSA, GA, and GHSA algorithms to 2.61, 1.72, 1.12, and 1.34 cents/h, respectively.
measurementThe hybrid genetic wind-driven (HGWD) algorithm developed by Javaid et al. (2017b) outperformed the Wind-Driven Optimization (WDO) algorithm and the Genetic Algorithm (GA) by lowering power usage costs by 33% and 10% respectively.