Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

Abdelhamid, Abdelaziz A.; El-kenawy, El-Sayed M.; Alrowais, Fadwa; Ibrahim, Abdelhameed; Khodadadi, Nima; Lim, Wei Hong; Alruwais, Nuha; Khafaga, Doaa Sami

doi:10.3390/en15239125

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…These algorithms explore the search space pseudo-randomly based on certain guiding principles without requiring gradient information. Due to their superior performance, lower computational requirements, and shorter processing times compared to deterministic algorithms, metaheuristic algorithms have gained popularity in various fields [1][2][3][4][5]. These algorithms rely on simple concepts and can quickly adapt to different domains.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Truss Structures Using Composite Materials under Natural Frequency Constraints with a New Hybrid Algorithm Based on Cuckoo Search and Stochastic Paint Optimizer (CSSPO)

et al. 2023

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This article highlights the absence of published paradigms hybridized by The Cuckoo Search (CS) and Stochastic Paint Optimizer (SPO) for optimizing truss structures using composite materials under natural frequency constraints. The article proposes a novel optimization algorithm called CSSPO for optimizing truss structures made of composite materials, known as fiber-reinforced polymer (FRP) composites, to address this gap. Optimization problems of truss structures under frequency constraints are recognized as challenging due to their non-linear and non-convex search spaces that contain numerous local optima. The proposed methodology produces high-quality optimal solutions with less computational effort than the original methods. The aim of this work is to compare the performance of carbon FRP (CFRP), glass FRP (GFRP), and steel using a novel hybrid algorithm to provide valuable insights and inform decision-making processes in material selection and design. Four benchmark structure trusses with natural frequency constraints were utilized to demonstrate the efficiency and robustness of the CSSPO. The numerical analysis findings indicate that the CSSPO outperforms the classical SPO and exhibits comparable or superior performance when compared to the SPO. The study highlights that implementing CFRP and GFRP composites in truss construction leads to a notable reduction in weight compared to using steel.

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Section: Introductionmentioning

confidence: 99%

Optimizing Truss Structures Using Composite Materials under Natural Frequency Constraints with a New Hybrid Algorithm Based on Cuckoo Search and Stochastic Paint Optimizer (CSSPO)

et al. 2023

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HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators

Singh,

Patra,

Pradhan

et al. 2024

Journal of Environmental Management

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Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Azizi

Shishehgarkhaneh

Basiri

et al. 2023

Sci Rep

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In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov–Smirnov, Mann–Whitney, and Kruskal–Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.

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Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

Cited by 5 publications

References 51 publications

Optimizing Truss Structures Using Composite Materials under Natural Frequency Constraints with a New Hybrid Algorithm Based on Cuckoo Search and Stochastic Paint Optimizer (CSSPO)

Optimizing Truss Structures Using Composite Materials under Natural Frequency Constraints with a New Hybrid Algorithm Based on Cuckoo Search and Stochastic Paint Optimizer (CSSPO)

HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

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