A novel hermit crab optimization algorithm

Guo, Jia; Zhou, Guoyuan; Ke, Yan; Shi, Binghua; Di, Yi; Sato, Yuji

doi:10.1038/s41598-023-37129-6

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Guo [31] proposed the TBBPSO algorithm, which enhances the local minimum escape capability of the proposed method. Guo [32] proposed BPSO-CM, where particle swarms are given enhanced global search capabilities. Xiao [33] proposed TMBPSO, where the particle swarm is endowed with the ability to self-correct.…”

Section: Related Workmentioning

confidence: 99%

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Chen,

et al. 2023

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Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.

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Section: Related Workmentioning

confidence: 99%

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Chen,

et al. 2023

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“…Among the notable algorithms in this category are Particle Swarm Optimization (PSO) 20 , Ant Colony Optimization (ACO) 21 , and Artificial Bee Colony (ABC) algorithm 22 , Tunicate Swarm Algorithm (TSA) 23 , Beluga Whale Optimization (BWO) 24 , Aphid-Ant Mutualism (AAM) 25 , artificial Jellyfish Search (JS) 26 , Spotted Hyena Optimizer (SHO) 27 , Honey Badger Algorithm (HBA) 28 , Mantis Search Algorithm (MSA) 29 , Nutcraker Optimization Algorithm (NOA) 30 , Manta Ray Foraging Optimization (MRFO) 31 , Orca Predation Algorithm (OPA) 32 , Yellow Saddle Goatfish (YSG) 33 , Hermit Crab Optimization Algorithm (HCOA) 34 , Cheetah Optimizer (CO) 35 , Walrus Optimization Algorithm (WaOA) 36 , Red-Tailed Hawk algorithm (RTH) 37 , Barnacles Mating Optimizer (BMO) 38 , Meerkat Optimization Algorithm (MOA) 39 , Snake Optimizer (SO) 40 , Grasshopper Optimization Algorithm (GOA) 41 , Social Spider Optimization (SSO) 42 , Whale Optimization Algorithm (WOA) 43 , Ant Lion Optimizer (ALO) 44 , Grey Wolf Optimizer (GWO) 45 , Marine Predators Algorithm (MPA) 46 ,Aquila Optimizer (AO) 47 , Mountain Gazelle Optimizer (MGO) 48 , Artificial Hummingbird Algorithm (AHA) 49 , African Vultures Optimization Algorithm (AVOA) 50 , Bonobo Optimizer (BO) 51 , Salp Swarm Algorithm (SSA) 52 , Harris Hawks Optimizer (HHO) 53 , Colony Predation Algorithm (CPA) 54 , Adaptive Fox Optimization (AFO) 55 , Slime Mould Algorithm (SMA) 56 , Spider…”

Section: Literature Reviewmentioning

confidence: 99%

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Amiri,

Hashjin,

Montazeri

et al. 2023

Preprint

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The novelty of this article lies in introducing a novel nonparametric metaheuristic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 132 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both local search and exploitation, as well as in global search and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. The performance of the HO consistently surpassed that of the top 3 algorithms in achieving optimal value, except for 29 functions. However, although it did not exhibit strong convergence in these 29 functions, the standard deviation for them was lower than the other investigated algorithms, illustrating its ability to manage the functions effectively. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The Wilcoxon signed test demonstrates that HO exhibits a notable and statistically significant advantage over the investigated algorithms in effectively addressing the optimization problems examined in this study.

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“…In 2023, Guo [23] proposed a cross-memory feature for PSO. Also, naturally inspired methods such as hermit crab optimization [24] demonstrate outstanding performance in single-objective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A Gated Recurrent Unit Model with Fibonacci Attenuation Particle Swarm Optimization for Carbon Emission Prediction

Guo,

Li,

Sato

et al. 2024

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Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for effectively training conventional forecasting models. To address this challenge, this paper introduces an innovative carbon emissions prediction model that integrates Fibonacci attenuation particle swarm optimization (FAPSO) with the gated recurrent unit (GRU). The FAPSO algorithm is used to optimize the hyperparameters of the GRU, thereby alleviating the decline in prediction accuracy that conventional recurrent neural networks often face when dealing with limited training data. To evaluate the effectiveness of the FAPSO-GRU model, we tested it using carbon emission data from Hainan Province. Compared to the conventional GRU model, the FAPSO-GRU model achieved a significant reduction in the mean absolute error (42.27%), root mean square error (42.38%), and mean absolute percentage error (43.06%). Furthermore, we validated the FAPSO-GRU model with real data from Beijing, Guangdong, Hubei, Hunan, and Shanghai. The experimental results convincingly demonstrate that the proposed model provides a highly accurate solution for carbon emission prediction tasks, effectively addressing the limitations posed by limited training data.

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A novel hermit crab optimization algorithm

Cited by 9 publications

References 40 publications

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

A Gated Recurrent Unit Model with Fibonacci Attenuation Particle Swarm Optimization for Carbon Emission Prediction

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