concept

Genetic Algorithm

Also known as: GA, Genetic Algorithms

Facts (46)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 29 facts

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

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.

claimBharathi et al. (2017) recommend combining genetic algorithms (GA) with load shifting techniques to reduce and reconfigure the load needs of energy consumers.

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.

referenceKaraboga N and Cetinkaya B (2004) compared the performance of genetic algorithms and differential evolution algorithms specifically for the design of digital finite impulse response (FIR) filters.

referenceKhalid A, Javaid N, Mateen A, Khalid B, Khan ZA, and Qasim U (2016) explored demand-side management using a combination of hybrid bacterial foraging and genetic algorithm optimization techniques, presented at the 10th International Conference on Complex, Intelligent, and Software Intensive Systems.

referenceAwais M, Javaid N, Shaheen N, Iqbal Z, Rehman G, Muhammad K, and Ahmad I published 'An efficient genetic algorithm based demand side management scheme for smart grid' in the proceedings of the 2015 18th International Conference on Network-Based Information Systems.

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.

claimManzoor et al. (2017) introduced the teacher learning genetic optimization (TLGO) method and compared it to the teacher learning-based optimization (TLBO) and Genetic Algorithm (GA) for residential load scheduling with a day-ahead pricing scheme.

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%.

referenceRoy C and Das DK published a study in 2021 titled 'A hybrid genetic algorithm (GA)–particle swarm optimization (PSO) algorithm for demand side management in smart grid considering wind power for cost optimization' in the journal Sādhanā.

referenceBharathi C, Rekha D, and Vijayakumar V published 'Genetic algorithm based demand side management for smart grid' in Wireless Personal Communications, volume 93, issue 2, pages 481–502, in 2017.

referenceBharathi C, Rekha D, and Vijayakumar V proposed a genetic algorithm-based approach for demand side management in smart grids in their 2017 paper published in Wireless Personal Communications.

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.

measurementManzoor et al. (2017) found that user discomfort was lowest with the teacher learning genetic optimization (TLGO) method compared to the Genetic Algorithm (GA) and teacher learning-based optimization (TLBO), with discomfort values of 2.37 for GA, 2.14 for TLBO, and 1.83 for TLGO.

measurementUsing Genetic Algorithm (GA) scheduling for home appliances reduces energy costs by 52% and peak-to-average ratio (PAR) by 23%, according to Ambreen et al. (2017).

referenceAwais et al. presented a paper titled 'An efficient genetic algorithm based demand side management scheme for smart grid' at the 2015 18th International Conference on Network-Based Information Systems.

referenceAmbreen K, Khalid R, Maroof R, Khan HN, Asif S, and Iftikhar H presented the paper 'Implementing critical peak pricing in home energy management using biography based optimization and genetic algorithm in smart grid' at the International Conference on Broadband and Wireless Computing, Communication and Applications in 2017.

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.

claimIn the context of energy management optimization, GA stands for Genetic algorithms.

measurementYang et al. (2015) found that the Improved Particle Swarm Optimization (IPSO) algorithm reduced peak load by approximately 30.26%, while the Genetic Algorithm (GA) reduced it by 25.78%.

measurementUsing genetic algorithm (GA) scheduling for home appliances results in a 52% reduction in costs and a 23% reduction in peak-to-average ratio (PAR), according to Ambreen et al. (2017).

measurementManzoor et al. (2017) reported cost reductions of 31%, 31.5%, and 33% produced by the Genetic Algorithm (GA), teacher learning-based optimization (TLBO), and teacher learning genetic optimization (TLGO), respectively.

claimBharathi et al. (2017) recommend combining Genetic Algorithms (GA) with load shifting techniques to reduce and reconfigure the load requirements of energy consumers.

referenceKaraboga N and Cetinkaya B (2004) conducted a performance comparison of genetic and differential evolution algorithms specifically for the design of digital finite impulse response (FIR) filters, presented at the International Conference on Advances in Information Systems.

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.

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.

A critical review on techno-economic analysis of hybrid renewable ... link.springer.com Springer Dec 6, 2023 10 facts

referenceMerei, Berger, and Sauer (2013) developed an off-grid compound photovoltaic-air current-diesel system using genetic algorithms to evaluate various battery technologies.

referenceThe University of Zaragoza in Spain created the Compound Development of Genetic Algorithm (HOGA), a tool that utilizes genetic algorithms to enhance system output.

claimIn studies of combined heat and power systems for residential applications, the particle swarm optimization (PSO) algorithm produced cost-effective solutions more quickly than the genetic algorithm (GA), although the genetic algorithm provided more promising results.

claimA compound air current and photovoltaic system developed in Jaipur, India, generates power independently using a Genetic Algorithm (GA) to provide an optimal and cost-effective solution for supplying power to variable loads.

claimGenetic Algorithms (GA) are a technique used to identify optimal system designs that minimize cost and the likelihood of power loss.

referenceDas et al. (2012) developed a Genetic Algorithm (GA) based frequency controller for a hybrid power system combining solar thermal, diesel, and air current (wind) generation with power storage.

measurementSalwe et al. performed Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) experiments on a photovoltaic/biomass/air current compound power system, finding that continuous charging resulted in costs of $0.2625/kWh (GA) and $0.2617/kWh (PSO), while cycle charging resulted in costs of $0.2396/kWh (GA) and $0.2393/kWh (PSO).

referenceSawle Y, Gupta SC, and Bohre AK published 'Optimal sizing of independent photovoltaic /Air current /Biomass compound power system using GA and PSO development technique' in Energy Procedia in 2017.

referenceGupta RA, Kumar R, and Bansal AK (2012) performed an economic analysis and design of a stand-alone hybrid air current (wind) and photovoltaic power system using a genetic algorithm.

procedureResearchers investigated the economic performance of combined heat and power systems for residential applications using iterative approaches, specifically mixed-integer linear programming models and heuristic techniques such as genetic algorithms (GA) and particle swarm optimization (PSO).

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 3 facts

referenceKumar et al. developed a hybrid optimization model combining Genetic Algorithms and Simulated Annealing to determine optimal electric vehicle charging station placements, addressing network resilience and distribution efficiency.

claimKumar et al. developed a hybrid optimization model combining Genetic Algorithms and Simulated Annealing to determine optimal electric vehicle charging station placements, addressing network resilience and distribution efficiency.

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 3 facts

referenceMellouk L., Boulmalf M., Aaroud A., Zine-Dine K., and Benhaddou D. utilized a genetic algorithm to solve demand-side management and economic dispatch problems in a 2018 study published in Procedia Computer Science.

claimThe complexity of Genetic Algorithms (GA) often results in high computational loads, which can hinder their use when user convenience is a secondary consideration.

claimGenetic Algorithms (GA) are employed to schedule household appliances to minimize energy costs by mimicking the process of natural selection to find efficient usage schedules.

Sustainable Energy Transition for Renewable and Low Carbon Grid ... frontiersin.org Frontiers Mar 23, 2022 1 fact

referenceViet, Phuong, Duong, and Tran published 'Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms' in the journal Energies in 2020.