Scheduling of Appliances in HEMS Using Elephant Herding Optimization and Harmony Search Algorithm

Parvez, Komal; Aslam, Sheraz; Saba, Arje; Aimal, Syeda; Amjad, Zunaira; Asif, Sikandar; Javaid, Nadeem

doi:10.1007/978-3-319-69811-3_6

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…Traveling salesman problem [134][135] Knapsack [136] Acoustic energy-based positioning [137][138][139][140][141][142][143] Scheduling [144] Electrostatic powder coating process [145][146] Image safety model [147][148][149][150] Image processing [151][152][153] Wireless sensor networks [154] Feature selection [155][156][157] Optimal power flow [158][159] Distribution systems [160] Constrained optimization Linea and nonlinear constrained optimization [161] Economic dispatch problems [162] Multi-objective optimization QoS aware web service composition optimization [163] Civil engineering [164] Structural optimization [165] Figure 5. Engineering optimization/applications.…”

Section: Continuous Optimizationmentioning

confidence: 99%

Elephant Herding Optimization: Variants, Hybrids, and Applications

et al. 2020

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Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algorithms has been demonstrated for many benchmark problems and in various application areas. A comprehensive review for the EHO-based algorithms and their applications are presented in this paper. Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future directions for research in the area of EHO are further discussed.

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Section: Continuous Optimizationmentioning

confidence: 99%

Elephant Herding Optimization: Variants, Hybrids, and Applications

et al. 2020

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“…Through extensive simulations, it is concluded that the proposed HEMS shows effectiveness with regards to PAR and cost reduction along with maximization of customer's comfort. In the work of Parvez et al, 26 the authors have proposed a support vector quantile regression‐based short‐term load probability density prediction method, where they also performed a comparison between three kernel functions, including Gaussian kernel, the linear kernel, and polynomial kernel. In addition, they evaluated the predicted precision of the power load using data sets from Singapore.…”

Section: Related Workmentioning

confidence: 99%

Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system

Aurangzeb

Aslam

Haider

et al. 2019

Trans Emerging Tel Tech

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Renewable energy resources (RERs) motivate electricity users to reduce their energy bills by taking benefit of self‐generated green energy. Different studies have already pointed out that, because of the absence of proper technical support and awareness, the energy users were not able to sufficiently take paybacks from the RERs. However, with the commencement of smart grids, the potential benefits of RERs and dynamic pricing schemes can be exploited. Nonetheless, the big issue is the accurate prediction of energy produced by intermittent RERs. In this work, we have proposed an efficient framework by integrating energy storage system (ESS) and RERs with smart homes. This framework has shown significant results, which make it helpful and suitable for energy management at a community level. We applied a multiheaded convolutional neural network model for precise and accurate prediction of produced energy by RERs. Moreover, we have considered a smart community consisting of 80 homes. Simulation results prove that the proposed framework helps to decrease the energy bill of consumers by 58.32% and 63.02% through integration of RERs without and with ESS, respectively.

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“…The EHO algorithm [Wang et al (2015b[Wang et al ( , 2016a, Parvez et al (2017), Tuba et al (2017), Strumberger et al (2017), Meena et al (2018), Hassanien et al (2018), Correia et al (2018), Elhosseini et al (2019)] had success in literature. However, some limitations are identified in EHO as follows [Ismaeel et al (2019)]: unjustified convergence toward the origin which has a negative effect on the exploration ability; the lack of exploration-exploitation trade-off control; and skewed distribution of initial elephants position.…”

Section: Preliminaries 21 Problem Formulationmentioning

confidence: 99%

“…Recently, EHO was applied to many optimization problems such as the distributed energy resources planning [Meena et al (2018)]. Another work illustrates the use of EHO in a home energy management system that minimizes electricity cost while allowing a desirable user waiting time [Parvez et al (2017)]. A method for automatic diseases using EHO to optimize the support vector machine parameters has been proposed in Tuba et al (2017).…”

Section: Introductionmentioning

confidence: 99%

An exploration-enhanced elephant herding optimization

Elshaarawy

Houssein

Ismail

et al. 2019

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Purpose The purpose of this paper is to propose an enhanced elephant herding optimization (EEHO) algorithm by improving the exploration phase to overcome the fast-unjustified convergence toward the origin of the native EHO. The exploration and exploitation of the proposed EEHO are achieved by updating both clan and separation operators. Design/methodology/approach The original EHO shows fast unjustified convergence toward the origin specifically, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms. Furthermore, the star discrepancy measure is adopted to quantify the quality of the exploration phase of evolutionary algorithms in general. Findings In experiments, EEHO has shown a better performance of convergence rate compared with the original EHO. Reasons behind this performance are: EEHO proposes a more exploitative search method than the one used in EHO and the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Operator γ is added to EEHO assists to escape from local optima, which commonly exist in the search space. The proposed EEHO controls the convergence rate and the random walk independently. Eventually, the quantitative and qualitative results revealed that the proposed EEHO outperforms the original EHO. Research limitations/implications Therefore, the pros and cons are reported as follows: pros of EEHO compared to EHO – 1) unbiased exploration of the whole search space thanks to the proposed update operator that fixed the unjustified convergence of the EHO toward the origin and the proposed separating operator that fixed the tendency of EHO to introduce new elephants at the boundary of the search space; and 2) the ability to control exploration–exploitation trade-off by independently controverting the convergence rate and the random walk using different parameters – cons EEHO compared to EHO: 1) suitable values for three parameters (rather than two only) have to be found to use EEHO. Originality/value As the original EHO shows fast unjustified convergence toward the origin specifically, the search method adopted in EEHO is more exploitative than the one used in EHO because of the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Further, the star discrepancy measure is adopted to quantify the quality of exploration phase of evolutionary algorithms in general. Operator γ that added EEHO allows the successive local and global searching (exploration and exploitation) and helps escaping from local minima that commonly exist in the search space.

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Scheduling of Appliances in HEMS Using Elephant Herding Optimization and Harmony Search Algorithm

Cited by 5 publications

References 12 publications

Elephant Herding Optimization: Variants, Hybrids, and Applications

Elephant Herding Optimization: Variants, Hybrids, and Applications

Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system

An exploration-enhanced elephant herding optimization

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