Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm

Niu, Peifeng; Chen, Ke; Ma, Yunpeng; Li, Xia; Liu, Aling; Li, Guoqiang

doi:10.1016/j.knosys.2016.11.011

Cited by 29 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…"The inertia weight of is controlled using the chaotic map. Tweaking the inertia weight of PSOs utilizing chaotic maps could really raise the likelihood of PSO escape from local neighbourhood optimum solution while resolving multimodal functions, so the logistic map is selected to tweak inertia weight of PSO" [7]. "The efficient decisions, anomaly, and non-repetition characteristics of the logistic map have the potential to increase wide range of population, and also enhance the functioning of converging at global optimum, avoiding untimely convergence" [8].…”

Section: Inertia Weightmentioning

confidence: 99%

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Ajibade

Chweya

Ogunbolu

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.

show abstract

Section: Inertia Weightmentioning

confidence: 99%

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Ajibade

Chweya

Ogunbolu

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In this paper, equation ( 16) is adopted in the velocity update mechanism. The sine map defined as ( 21) is selected to tune inertia weight defined w [31,32], and the range of the sine map is from 0 to 1. The SBAC strategy in reference [22] defined by ( 22) and ( 23) is used to balance the local search and the global search.…”

Section: ) Velocity Update Mechanismmentioning

confidence: 99%

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Kong

Tongle

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied to the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. And the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the preexisting algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.INDEX TERMS Adaptive wavelet threshold, dynamic weight particle swarm optimization, fault diagnosis, sigmoid-based acceleration coefficients, wind turbine bearing.

show abstract

“…Previous research has shown that the sine function plays a great role in the adjustment of ω [48,49]. Combining the sine function and chaos mechanism, this paper uses a sine iterator to directly generate the sequence to achieve the sine chaotic inertial weight.…”

Section: Sine Chaotic Inertia Weight ωmentioning

confidence: 99%

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Fang

2020

Symmetry

View full text Add to dashboard Cite

As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

show abstract

Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm

Cited by 29 publications

References 23 publications

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Contact Info

Product

Resources

About