A Comparative Study on PSO with Other Metaheuristic Methods

Yarat, Serhat; Senan, Sibel; Orman, Zeynep

doi:10.1007/978-3-030-70281-6_4

Cited by 14 publications

(4 citation statements)

References 65 publications

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“…Negative values indicate an inverse relationship between the main index and the features; in contrast, a positive value indicates a direct relationship. The operational principle is illustrated in Equation (28).…”

Section: External Corrosion Influencing Factors Of Buried Pipelinesmentioning

confidence: 99%

“…In addition, there are various models and algorithms in the field of corrosion prediction [22]. These include artificial neural network models (such as FF, MLP, and RBF) [23][24][25] and optimization algorithms (such as WOA, Bat, Firefly, and GWO) [26][27][28]. Compared to other optimization algorithms (e.g., WOA, Bat, and Firefly), PSO has faster convergence and global search capability [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Ren,

Chen,

Yang

et al. 2024

Applied Sciences

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External corrosion poses a significant threat to the integrity and lifespan of buried pipelines. Accurate prediction of corrosion rates is important for the safe and efficient transportation of oil and natural gas. However, limited data availability often impacts the performance of conventional predictive models. This study proposes a novel composite modeling approach integrating kernel principal component analysis (KPCA), particle swarm optimization (PSO), and extreme learning machine (ELM). The key innovation lies in using KPCA for reducing the dimensionality of complex input data combined with PSO for optimizing the parameters of the ELM network. The model was rigorously trained on 12 different datasets and comprehensively evaluated using metrics such as the coefficient of determination (R2), standard deviation (SD), mean relative error (MRE), and root mean square error (RMSE). The results show that KPCA effectively extracted four primary components, accounting for 91.33% of the data variability. The KPCA-PSO-ELM composite model outperformed independent models with a higher accuracy, achieving an R2 of 99.59% and an RMSE of only 0.0029%. The model comprehensively considered various indicators under the conditions of limited data. The model significantly improved the prediction accuracy and provides a guarantee for the safety of oil and gas transport.

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Section: External Corrosion Influencing Factors Of Buried Pipelinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Ren,

Chen,

Yang

et al. 2024

Applied Sciences

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“…The current mainstream methods include the Genetic Algorithm, Particle Swarm Optimization, the Differential Evolution algorithm, the Exponential Decay algorithm, and the Cosine Annealing algorithm. The Genetic Algorithm, Particle Swarm Optimization, Differential Evolution algorithm, and Exponential Decay algorithm are prone to premature convergence and poor convergence ability for high-dimensional complex problems of multi-neural network cooperation [34,35], especially the Particle Swarm Optimization algorithm, which is very prone to premature convergence [36]. Therefore, this paper selected the Cosine Annealing algorithm [37] to optimize the learning rate based on the fusion optimization of the feature layer, and the effectiveness of the method for the results was verified by experiments.…”

Section: Learning Rate Optimization Based On Cosine Annealing Algorithmmentioning

confidence: 99%

Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model

Liu,

Zuo,

Wang

et al. 2023

Applied Sciences

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Entity relation extraction mainly extracts relations from text, which is one of the important tasks of natural language processing. At present, some special fields have insufficient data; for example, agriculture, the metallurgical industry, etc. There is a lack of an effective model for entity relationship recognition under the condition of insufficient data. Inspired by this, we constructed a suitable small balanced data set and proposed a multi-neural network collaborative model (RBF, Roberta–Bidirectional Gated Recurrent Unit–Fully Connected). In addition, we also optimized the proposed model. This model uses the Roberta model as the coding layer, which is used to extract the word-level features of the text. This model uses BiGRU (Bidirectional Gated Recurrent Unit)–FC (Fully Connected) as the decoding layer, which is used to obtain the optimal relationship of the text. To further improve the effect, the input layer is optimized by feature fusion, and the learning rate is optimized by the cosine annealing algorithm. The experimental results show that, using the small balanced data set, the F1 value of the RBF model proposed in the paper is 25.9% higher than the traditional Word2vec–BiGRU–FC model. It is 18.6% higher than the recent Bert–BiLSTM (Bidirectional Long Short Term Memory)–FC model. The experimental results show that our model is effective.

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“…To allocate multiple DG units in a distribution network in the most effective way, a powerful optimization technique based on the sine cosine algorithm (SCA) and chaos map theory was proposed [28][29][30][31]. The genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), artificial bee colony (ABC), harmony search (HS), gray wolf optimization algorithm, and backtracking search optimization algorithm have all been mentioned in this paper as being used to determine the ideal size and location of DG units [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods

et al. 2023

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The need for electrical energy is increasing in line with the increase in population and increasing progress in welfare. On the other hand, the availability of fossil fuels as the main fuel in generating electricity is dwindling; so, there is a need for policies that require the use of environmentally friendly renewable energy. The utilization of renewable energy does not necessarily apply freely due to several constraints. One effort is a generator or distributed generation (DG) which is placed in the distribution line close to the load. The utilization of DG must go through planning, especially the large capacity and position on the bus and on the feeder, which will result in small network losses and a voltage profile that meets tolerance limits. Thus, the purpose of this study is to optimize to obtain the capacity and location of the DG calculated by considering the variation in the load through the genetic algorithm method. As a result, the optimal DG position for normal load is obtained on bus 18, bus 20, and bus 32 with capacities of 190 kW, 463 kW, and 370 kW, respectively. The losses obtained decreased from 54.6733 kW to 9.9447 kW, and the voltage profile was maintained within the specified limits. Optimization was carried out for decreasing and increasing loads in percent. The result is that losses can be minimized, and the voltage profile remains within the required limits. The lower the load, the more stable the voltage and the smaller the losses; meanwhile, the larger the load, the more fluctuating the voltage is, but still within the limits specified in the optimization.

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A Comparative Study on PSO with Other Metaheuristic Methods

Cited by 14 publications

References 65 publications

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique

Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model

Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods

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