Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Kullampalayam Murugaiyan, Nandhini; Chandrasekaran, Kumar; Manoharan, Premkumar; Derebew, Bizuwork

doi:10.1038/s41598-023-50890-y

Cited by 17 publications

(2 citation statements)

References 89 publications

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“…The employment of meta-heuristic optimization algorithms has demonstrated promise in efficiently optimizing neural network parameters 35 , 36 . These algorithms provide an effective and flexible means of exploring high-dimensional search spaces while avoiding local optima, thereby preventing suboptimal solutions 37 . The integration of meta-heuristic optimization techniques into neural network parameter tuning has shown significant enhancements in overall performance and accuracy 38 .…”

Section: Introductionmentioning

confidence: 99%

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Cui,

Ding,

et al. 2024

Sci Rep

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Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional $$l_2$$ l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.

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Section: Introductionmentioning

confidence: 99%

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Cui,

Ding,

et al. 2024

Sci Rep

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“…These algorithms have proven their reliability and effectiveness in discovering optimal solutions to complex real-world problems. In addition to classical numerical optimization, metaheuristic algorithms have demonstrated their efficacy in a wide range of optimization tasks, including, but not limited to, feature selection [ 1 , 2 ], the traveling salesman problem [ 3 , 4 ], image segmentation problems [ 5 , 6 , 7 ], wireless sensor coverage problems [ 8 , 9 ], parameter estimation for solar photovoltaic models [ 10 , 11 ], and path planning [ 12 , 13 ]. In the past decades, new metaheuristic algorithms have been continuously proposed that are based on evolutionary theory, physical laws, biological population behavior, and human behavior.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Wang,

Zhang,

Zou

2024

Biomimetics

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The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish’s summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm’s ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems.

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An efficient improved exponential distribution optimizer: application to the global, engineering and combinatorial optimization problems

Houssein,

Saeed,

et al. 2024

Cluster Comput

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Population-based meta-heuristic optimization algorithms play a vital role in addressing optimization problems. Nowadays, exponential distribution optimizer (EDO) can be considered to be one of the most recent among these algorithms. Although it has achieved many promising results, it has a set of shortcomings, for example, the decelerated convergence, and provides local optima solution as it cannot escape from local regions in addition to imbalance between diversification and intensification. Therefore, in this study, an enhanced variant of EDO called mEDO was proposed to address these shortcomings by combining two efficient search mechanisms named orthogonal learning (OL) and local escaping operator (LEO). In mEDO, the LEO has been exploited to escape local optima and improve the convergence behavior of the EDO by employing random operators to maximize the search process and to effectively discover the globally optima solution. Then the OL has been combined to keep the two phases (i.e., exploration and exploitation) balanced. To validate the effectiveness and performance of the mEDO algorithm, the proposed method has been evaluated over ten functions of the IEEE CEC’2020 test suite as well as eight real-world applications (engineering design optimization problems), Furthermore we test the applicability of the proposed algorithm by tackling 21 instance of the quadratic assignment problem (QAP). The experimental and statistical results of the proposed algorithm have been compared against seven other common metaheuristic algorithms (MAs), including the basic EDO. The results show the supremacy of the mEDO algorithm over the other algorithms and reveal the applicability and effectiveness of the mEDO algorithm compared to well-established metaheuristic algorithms. The experimental results and different statistical measures revealed the reliability and applicability of the mEDO method in solving the global, engineering design, and combinatorial optimization problems by achieving a reasonable solution in terms of scoring a global optima solutions and avoiding premature convergence by increasing the population’s diversity.

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Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Cited by 17 publications

References 89 publications

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

An efficient improved exponential distribution optimizer: application to the global, engineering and combinatorial optimization problems

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