A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

Xiao, Haiyang; Guo, Jia; Shi, Binghua; Di, Yi; Pan, Chao; Ke, Yan; Sato, Yuji

doi:10.1109/access.2022.3222530

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…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. Li [34] proposed an optimized VMD and ELM, the ISGSP, to construct a decomposition-prediction to forecast carbon emissions.…”

Section: Related Workmentioning

confidence: 99%

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

Chen,

et al. 2023

Processes

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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

Processes

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“…Subsequently, in 2022, Tian expanded BBPSO by incorporating a transition operator and an orbit merging operator 31 . Then, in 2023, Xiao introduced TMBBPSO 32 , which integrates two memory mechanisms into BBPSO, tailored for solving nonlinear problems. In 2016, Yong introduced the Dolphin Swarm Optimization Algorithm (DSOA) 33 , which simulates the social and hunting behaviors of barracudas within the search area.…”

Section: Introductionmentioning

confidence: 99%

Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems

Guo,

Zhou,

Yan

et al. 2023

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High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm’s performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges.

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“…In 2022, Tian 31 adds a transition operator and an orbit merging operator to the BBPSO. In 2023, Xiao 32 proposed TMBBPSO, which applies two memory mechanisms to BBPSO for solving nonlinear problems. In 2016, Yong 33 proposed a barracuda swarm optimization algorithm (DSOA).…”

Section: Introductionmentioning

confidence: 99%

Pair barracuda swarm optimization algorithm a natural-inspired metaheuristic method for high dimensional optimization problems

Guo,

Zhou,

et al. 2023

Preprint

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The high-dimensional optimization is a new research difficulty in the field of intelligent computing.Traditional evolutionary tools are prone to dimensional catastrophes and local optimal in high dimensional spaces, thus failing to provide highly accurate results. In this paper, to solve the high-dimensional optimization problem, a novel pair barracuda swarm optimization algorithm (PBSOA) is proposed. In PBSOA, a novel construction of barracuda pairs is proposed to reduce dimensional catastrophes.A support barracuda is also used to enhance the global search ability of the leader barracuda pair.To verify the performance of the barracuda algorithm, the CEC2017 standard function is used for experiments, and five excellent optimization algorithms are selected in control groups. Finally, in a total of 29 test functions, PBSOA gets 18 firsts, 4 seconds, 3 thirds, 3 fourths, and 1 sixth, and the average rank is 1.83.The experimental results demonstrate that PBSOA presents the best performance in all algorithms and is able to provide trustworthy results for high-dimensional optimization problems.

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A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

Cited by 6 publications

References 32 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

Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems

Pair barracuda swarm optimization algorithm a natural-inspired metaheuristic method for high dimensional optimization problems

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