Energy Optimization in Smart Grid Using Grey Wolf Optimization Algorithm and Bacterial Foraging Algorithm

Charaf, Hassan; Khan, Muhammad Sufyan; Ghafar, Asad; Aimal, Syeda; Asif, Sikandar; Javaid, Nadeem

doi:10.1007/978-3-319-65636-6_15

Cited by 8 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [23] proposed an HEMS for cost reduction and load balancing. The performance of HEMS is evaluated by this work using grey wolf optimization (GWO) and BFOA.…”

mentioning

confidence: 99%

Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

et al. 2018

Self Cite

View full text Add to dashboard Cite

With the emergence of the smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In this work, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a home energy management controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the utility with the load profile of the home. The smart meter is connected to a power grid having an advanced metering infrastructure which is responsible for two-way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising user comfort. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and real-time pricing information. Simulation results show that proposed algorithms reduce the peak-to-average ratio by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.

show abstract

“…The authors in [23] proposed an HEMS for cost reduction and load balancing. The performance of HEMS is evaluated by this work using grey wolf optimization (GWO) and BFOA.…”

mentioning

confidence: 99%

Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…1) The meta-heuristic algorithms Grey Wolf Optimization (GWO) [36,47] and StrawBerry (SB) algorithm [37, 38, 68 -70], support solving multi-variable problems, and have been applied for the optimization of various engineering problems. In this paper, GWO and SB meta-heuristic algorithms are applied for having logical, and acceptable parametric models for software effort estimation.…”

Section: B Contributionsmentioning

confidence: 99%

“…Grey wolf optimization is unprecedented nature inspired algorithm, which is based on the natural behavior of grey wolves. It is valuable for searching optimized solutions [36,47]. Grey wolves live in packs and based on their behavior categorized in four types namely Alpha (α), Beta (β ), Delta (δ ) and Omega (ω) respectively.…”

Section: ) Grey Wolf Algorithmmentioning

confidence: 99%

“…Grey wolf optimization algorithm is used to solve the optimization problems [36,47]. In the initial stage, the search agents are initialized.…”

Section: ) Grey Wolf Algorithmmentioning

confidence: 99%

“…The rising trend of using meta-heuristic algorithms has been witnessed in the recent literature to tweak COCOMO coefficients as well as to optimize software effort estimation [30][31][32][33][34]. These meta-heuristic algorithms performed well in dealing with the optimization problem in different domains-of-interest [36][37][38]42], because of their unique features including large searching space and random selection technique.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

et al. 2021

Self Cite

View full text Add to dashboard Cite

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

show abstract

Analysis and Comparison of Swarm Intelligence Algorithm in IoT: A Survey

Jain

Agarwal

2022

Proceedings of the Third International Conference on Information Management and Machine Intelligence

View full text Add to dashboard Cite

Energy Optimization in Smart Grid Using Grey Wolf Optimization Algorithm and Bacterial Foraging Algorithm

Cited by 8 publications

References 10 publications

Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Analysis and Comparison of Swarm Intelligence Algorithm in IoT: A Survey

Contact Info

Product

Resources

About