A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization

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

doi:10.1109/access.2023.3250228

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…To further prove the superiority of the HCOA algorithm in high-dimensional optimization problems, we choose the state-of-the-art method, the BPSO-CM 40 algorithm, as the control group, to conduct experiments on the highest dimension of 100 recommended by CEC2017. To minimize the effect of chance errors on the experimental results, all the trials are attempted 37 times with a population size of 100 and a maximum number of iterations of 1.00E+4.…”

Section: Resultsmentioning

confidence: 99%

A novel hermit crab optimization algorithm

Guo

Zhou

Ke³

et al. 2023

Sci Rep

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High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.

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Section: Resultsmentioning

confidence: 99%

A novel hermit crab optimization algorithm

Guo

Zhou

Ke³

et al. 2023

Sci Rep

Self Cite

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“…In addition, a backtracking algorithm was introduced into the algorithm for reclassification as a way to correct samples that were misclassified during the former classification process. Also, novel evolutionary methods 36 – 38 can be used in complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning

Guo,

Yuan,

Shi

et al. 2024

Sci Rep

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Assistive medical image classifiers can greatly reduce the workload of medical personnel. However, traditional machine learning methods require large amounts of well-labeled data and long learning times to solve medical image classification problems, which can lead to high training costs and poor applicability. To address this problem, a novel unsupervised breast cancer image classification model based on multiscale texture analysis and a dynamic learning strategy for mammograms is proposed in this paper. First, a gray-level cooccurrence matrix and Tamura coarseness are used to transfer images to multiscale texture feature vectors. Then, an unsupervised dynamic learning mechanism is used to classify these vectors. In the simulation experiments with a resolution of 40 pixels, the accuracy, precision, F1-score and AUC of the proposed method reach 91.500%, 92.780%, 91.370%, and 91.500%, respectively. The experimental results show that the proposed method can provide an effective reference for breast cancer diagnosis.

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“…To enhance the optimization ability, Guo [22] combined the twining bare bones strategy with standard PSO. 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

Processes

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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 Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization

Cited by 4 publications

References 29 publications

A novel hermit crab optimization algorithm

A novel hermit crab optimization algorithm

A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning

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

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