Improvement and Application of Fractional Particle Swarm Optimization Algorithm

Li, Jing; Zhao, Chunna

doi:10.1155/2022/5885235

Cited by 3 publications

(6 citation statements)

References 27 publications

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“…GDIWPSO, SAPSO, OscTri, S-PSO, and IPSO are from the cited references [16][17][18][19][20], respectively, and they are improved algorithms for linearly decreasing inertia weights based on PSO and combined with various formulas respectively. They are compared with GA, which, like PSO, was proposed to solve combinatorial optimization problems.…”

Section: Comparison and Analysis Of Experimental Results Between Caps...mentioning

confidence: 99%

“…The inertia weight of the strategy increases from 0.4 to 0.9 in the form of a sinusoidal curve, and then decreases to 0.4 in the form of a sinusoidal curve. Li et al [20] proposed a nonlinear decreasing strategy of inertia weight based on the inverse incomplete Γ function, which is close to an exponential decrease in the later stage, which can better achieve a balance between global search ability and local search ability. In this paper, the above ideas are used to optimize the injection-production parameters by improving the particle swarm algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm

et al. 2022

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The optimization of injection–production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particle swarm algorithm, the net present value of the improved scheme was increased by about 3.3%.

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Section: Comparison and Analysis Of Experimental Results Between Caps...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm

et al. 2022

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“…We are starting with the work of Chou et al [69], which presents a fractional order particle swarm optimization to improve the classification of heart diseases via an XGBoost classifier. Furthermore [70] presented an improved fractional particle swarm optimization algorithm and shows its applicability to optimize support vector machine and K-Means algorithms. As used in the same domain, the researchers used a dataset where the task is to classify heart diseases.…”

Section: Fractional Gradient-free Optimizationmentioning

confidence: 99%

Combining Fractional Derivatives and Machine Learning: A Review

Raubitzek

Mallinger²,

Neubauer³

2022

Entropy

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Fractional calculus has gained a lot of attention in the last couple of years. Researchers have discovered that processes in various fields follow fractional dynamics rather than ordinary integer-ordered dynamics, meaning that the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of which is that fractional derivatives inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It has provided many further insights and discoveries in various scientific disciplines. As these two modern-day topics hold a lot of potential for combined approaches in terms of describing complex dynamics, this article review combines approaches from fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there is already extensive literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning and fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools, and enhance their existing approaches.

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“…We're starting with the work of Chou et al [68], which presents a fractional order particle swarm optimization to improve the classification of heart diseases via an XGBoost classifier. Further, [69] presents an improved fractional particle swarm optimization al-gorithm and shows its applicability to optimize support vector machine and K-Means algorithms. As used in the same domain, the researchers use a dataset where the task is to classify heart diseases.…”

Section: Fractional Gradient-free Optimizationmentioning

confidence: 99%

“…For the gradient-free optimization algorithms, we highlight the work done by Li et al [69], where the researchers present an improved fractional particle swarm optimization algorithm to improve a support vector machine and k-Means-based classification of heart diseases.…”

Section: Optimizationmentioning

confidence: 99%

Combining Fractional Derivatives and Supervised Machine Learning: A Review

Raubitzek¹,

Mallinger²,

Neubauer³

2022

Preprint

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Fractional calculus gained a lot of attention in the last couple of years. Researchers discovered that processes in various fields follow rather fractional dynamics than ordinary integer-ordered dynamics, meaning the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of them being that fractional derivatives’ inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It further provides many insights and discoveries in various scientific disciplines. As these two modern-day topics provide a lot of potential for combined approaches to describe complex dynamics, this article reviews combined approaches of fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there already is profound literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning &amp; fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools and enhance their existing approaches.

show abstract

Improvement and Application of Fractional Particle Swarm Optimization Algorithm

Cited by 3 publications

References 27 publications

Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm

Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm

Combining Fractional Derivatives and Machine Learning: A Review

Combining Fractional Derivatives and Supervised Machine Learning: A Review

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