Optimal Combination and Sizing of a Stand–Alone Hybrid Energy System Using a Nomadic People Optimizer

Mohammed, Abbas Q.; Al-Anbarri, Kassim A.; Hannun, Rafid M.

doi:10.1109/access.2020.3034554

Cited by 33 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of them inspired from a biological organism or social life, such as artificial bee colony (ABC), ant colony optimizer (ACO), FA, grey wolf optimizer (GWO), ant lioner optimizer (ALO), and nomadic people optimizer (NPO) [15]- [20]. NIAs have played a great role for solving different types of optimization problems, medical case studies [21]- [23], engineering [24]- [36], energy [37]- [48], and information security [49]- [51]. Salp swarm algorithm (SSA) is a recent nature inspired algorithm, which is inspired from the cylindrical jellyfishes-like creatures which are belong to Salpidae [52]- [55].…”

Section: Nature-inspired Algorithms and Salp Swam Algorithmmentioning

confidence: 99%

A missing data imputation method based on salp swarm algorithm for diabetes disease

Hassan

Ali²,

Abdulsahib

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayesian classifier (NBC) have been enhanced as compared to the dataset before applying the proposed method. Moreover, the results indicated that issa was performed better than the statistical imputation techniques such as deleting the samples with missing values, replacing the missing values with zeros, mean, or random values.

show abstract

Section: Nature-inspired Algorithms and Salp Swam Algorithmmentioning

confidence: 99%

A missing data imputation method based on salp swarm algorithm for diabetes disease

Hassan

Ali²,

Abdulsahib

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…The LCC of the system includes initial cost (IC), maintenance cost (MC), and replacement cost (RC), obtained from Equations ( 10)-( 13) 40 :…”

Section: Life Cycle Costmentioning

confidence: 99%

“…The LCC of the system includes initial cost (IC), maintenance cost (MC), and replacement cost (RC), obtained from Equations () 40 :

{LCC}_{n} goodbreak= {IC}_{n} goodbreak+ {MC}_{n} goodbreak+ {RC}_{n} \forall n \in ([],,, 1, \dots, N^{HRES})

{IC}_{n} goodbreak= ((), C_{I}^{WT} goodbreak\times N_{n}^{WT}) goodbreak+ ((), C_{I}^{PV} goodbreak\times N_{n}^{PV}) goodbreak+ ((), C_{I}^{b} goodbreak\times N_{n}^{b}) \forall n \in ([],,, 1, \dots, N^{HRES})

\begin{matrix} right center left3pttrue \\ {normalMC}_{n} & = & (C_{normalM}^{normalWT} \times N_{n}^{normalWT} + C_{normalM}^{normalPV} \times N_{n}^{normalPV} + C_{normalM}^{normalb} \times N_{n}^{normalb}) \\ left \times {false\sum}_{t = 1}^{T} {(\frac{1 + InfR}{1 + IntR})}^{t} \forall n \\ left \times \in [1, \dots, N^{HRES}] \forall t \times \in [1, \dots, 24] \end{matrix}

\begin{matrix} lefttrue \\ {RC}_{n} = (C_{normalR}^{normalb} \times N_{n}^{normalb}) {false\sum}_{t = 1}^{T} ((), \frac{1 + InfR}{1 + IntR}) \end{matrix}

…”

Section: Mathematical Modellingmentioning

confidence: 99%

Optimal sizing of hybrid renewable energy systems by considering power sharing and electric vehicles

Sadeghi

Amiri

Marzband

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

Summary Currently, the ideal sizing of hybrid technologies is one of the vital aspects of power system design. In this article, the design and optimization of the sizing of hybrid renewable energy systems (HRESs) with power‐sharing capabilities in conjunction with electric vehicles (EVs) were proposed in two case studies. Two algorithms, namely, multi‐objective particle swarm optimization (MOPSO) and multi‐objective crow search (MOCS), have been formulated and were used to solve the problem being investigated. In case study 1 (CS1), four different HRESs are designed in the presence of EVs, meaning that for each HRES an EV and the power‐sharing capability is employed. And also, the stochastic behavior of the EV using Monte Carlo simulation (MCS) is modeled. In case study 2 (CS2), four HRESs are designed with power‐sharing capabilities, but in this case, for any of the HRESs, EV is not considered. This idea can be considered a novel breakthrough for the potential of power‐sharing has been incorporated with the integration of EVs and HRESs. This approach improves the life cycle cost and loss of power supply probability indices. In summary, both cases in the presence and absence of EVs were compared with the simulation results. The results show that the use of the proposed EV significantly reduces the total cost of the engineered system. Furthermore, two meta‐heuristic techniques were compared, and it was concluded that MOPSO had performed better than MOCS. Highlights Optimal sizing and power sharing of hybrid renewable systems with EVs. Proposed novel heuristic optimization approach using MOPSO and MOCS. Optimization of uncertainty parameters using 100 different scenarios using MCS. Economic and reliability benefits of the proposed system.

show abstract

“…Metaheuristics have been implemented for handling different types of optimization problems, such as machine learning [6][7][8], Engineering Problems [9], Data Clustering, and Green Energy [10][11][12][13]. Nevertheless, many metaheuristic-based design algorithms (such as firefly algorithm) have been proposed for constructing 8 Â 8 S-boxes [14].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Particle Swarm Optimization Based on Meeting Room Approach for Designing Bijective S-Boxes

Alhadawi

Salih

Salman

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

View full text Add to dashboard Cite

This paper proposed a modified and enhanced multi-swarm Particle Swarm Optimization algorithm based on Meeting Room Approach (MPSO) with Tent Logistic map. The proposed modified MPSO algorithm was used to generate strong 8 Â 8 substitution boxes (S-Box) while a Chaotic Tent map was used to update the velocities of each leader in the meeting room to improve their movements and enhance the exploration capability of the MPSO. The generated S-box was validated using cryptographic performance evaluation criteria, such as nonlinearity, bijectivity, strict avalanche criteria, bit independence criteria, differential probability, and linear approximation probability. The obtained results showed that the proposed algorithm performed better than some of the state-of-the-art algorithms and proved its capability against cryptanalysis attacks.

show abstract

Optimal Combination and Sizing of a Stand–Alone Hybrid Energy System Using a Nomadic People Optimizer

Cited by 33 publications

References 36 publications

A missing data imputation method based on salp swarm algorithm for diabetes disease

A missing data imputation method based on salp swarm algorithm for diabetes disease

Optimal sizing of hybrid renewable energy systems by considering power sharing and electric vehicles

Chaotic Particle Swarm Optimization Based on Meeting Room Approach for Designing Bijective S-Boxes

Contact Info

Product

Resources

About