Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast

Xing, Qianyi; Wang, Jianzhou; Lu, Haiyan; Wang, Shuai

doi:10.1016/j.enconman.2022.115583

Cited by 26 publications

(9 citation statements)

References 55 publications

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“…Xing et al [ 20 ] introduced a novel wind speed prediction system that integrates data pre-processing, benchmark model selection, an AO algorithm for point and interval forecast. This research shows that the weights assigned by this optimizer are Pareto optimum solutions.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

“…A multi-objective AO was presented by Xing et al [ 20 ] in order to locate the optimal weights, and a theoretical demonstration suggests that the weights supplied by this optimizer are Pareto optimal solutions. The results of the experiments showed that the designed method achieved greater accuracy than the examined methods in every case for point forecasting, and achieved a forecasting interval with great coverage and minimal error.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Wang et al 27 used improved AO to optimize the configuration of hybrid SOFC, gas turbine, and Proton Exchange Membrane Electrolyzer. Xing et al 28 proposed multi‐objective AO to search for the optimal weight and proved that the weights are Pareto optimal solutions for short‐term wind speed prediction. Ekinci et al 29 improved AO using modified opposition‐based learning mechanism and Nelder‐Mead simplex search method for control design approach in automatic voltage regulator.…”

Section: Related Workmentioning

confidence: 99%

Aquila optimizer with dynamic group strategy for global optimization tasks

Deng

2023

Concurrency and Computation

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SummaryThe Aquila optimizer (AO) is an efficient method for solving global optimization problems. However, the evolution of each individual learns from experience in the same group, which can easily fall into local optima. Therefore, this paper adopts the dynamic grouping strategy (DGS) of the population and proposes an improved AO algorithm to solve the global optimization problem. Different from the original AO algorithm, the DGSAO algorithm only evolves the individuals with the worst fitness in each group each time, which increases the diversity of the population. In order to verify the effectiveness of the algorithm, we tested it on 23 benchmark functions, among which the dimensions of to are 30, 100, and 200 dimensions. The experimental results show that the DGSAO algorithm is an effective method for solving global optimization problems. At the same time, we also conduct experiments on two engineering design problems, and the results show that the DGSAO algorithm can obtain a competitive result.

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“…In 17 , Ebola Optimization Search Algorithm (EOSA) 18 is used to evaluation the performance convolutional neural networks. In 19 , Aquila Optimizer (AO) 20 is used for weights allocated of forecasting model. In 21 , Discrete Equilibrium Optimizer and Simulated Annealing is hybridized to solved structural optimization and multi-level image segmentation problems.…”

Section: Optimization Of Complex Engineering Problems Using Modified ...mentioning

confidence: 99%

Optimization of complex engineering problems using modified sine cosine algorithm

Chen

Zhou

Liu

2022

Sci Rep

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In this article, a modified version of the Sine Cosine algorithm (MSCA) is proposed to solve the optimization problem. Based on the Sine Cosine algorithm (SCA), the position update formula of SCA is redefined to increase the convergence speed, then the Levy random walk mutation strategy is adopted to improve the population diversity. In order to verify the performance of MSCA, 24 well-known classical benchmark problems and IEEE CEC2017 test suites were introduced, and by comparing MSCA with several popular methods, it is demonstrated that MSCA has good convergence and robustness. Finally, MSCA is used to address six complex engineering design problems, demonstrating the engineering utility of the algorithm.

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Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast

Cited by 26 publications

References 55 publications

A Comprehensive Survey on Aquila Optimizer

A Comprehensive Survey on Aquila Optimizer

Aquila optimizer with dynamic group strategy for global optimization tasks

Optimization of complex engineering problems using modified sine cosine algorithm

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