Heterogeneous Acceleration of Hybrid PSO-QN Algorithm for Neural Network Training

Shi, Yan; Liu, Qiang; Li, Jiajun; Han, Liang

doi:10.1109/access.2019.2951710

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…By 2019, MCPSO, a modified centralized algorithm based on PSO, was generated in which the MCPSO assigns tasks to supply medicine and food for the victims in specific places using unmanned aerial vehicles [15]. Another application of that year is the artificial neural network (ANN) training to find the weights of the network, implementing PSO and quasi-Newton (QN) on the CPU-GPU platform with OpenCL [102].…”

Section: Pso Applicationsmentioning

confidence: 99%

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

et al. 2022

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Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research.

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Section: Pso Applicationsmentioning

confidence: 99%

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

et al. 2022

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“…The authors of the large majority of the considered papers used this approach as displayed in Tables 3, 4 and 6. This strategy is easy to implement and it is capable of reducing considerably the execution time of the algorithm [25,[28][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Particle-level Parallelizationmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

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“…Every particle cannot achieve globally best fitness, called Gbest. Every single particle track and memorize its current best fitness in the swarm [64]. In this proposed hybrid model, the solution vector of PSO consists of weights and biases of ANN model.…”

Section: Proposed Hybrid Artificial Neural Network Prediction Model For Wind Power Densitymentioning

confidence: 99%

“…For best training of ANN, weights and biases are predicted by PSO algorithms. In this hybrid model, PSO improves the architecture of the Artificial Neural Network (ANN) as its training is based on trial and error [65]. In the PSO algorithm, each particle is accelerated in each time step toward Pbest and Gbest by using random weights.…”

Section: Proposed Hybrid Artificial Neural Network Prediction Model For Wind Power Densitymentioning

confidence: 99%

One-Month-Ahead Wind Speed Forecasting Using Hybrid AI Model for Coastal Locations

et al. 2020

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Wind speed forecasts can boost the quality of wind energy generation by increasing the efficiency and enhancing the economic viability of this variable renewable resource. This work proposes a hybrid model for wind energy capacity for electrical power generation at coastal sites by utilizing windrelated variables' characteristics. The datasets of three coastal locations of Kuwait validate the proposed method. The hybrid model is a merger of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) and predicts one-month-ahead wind speed for wind power density calculation. The neural network starts its performance evaluation with a variable number of hidden-layer neurons to finally identify the optimal ANN topology. Comparisons of statistical indices with both expected and observed test results indicate that the ANN-PSO based hybrid model with the low root-mean-square-error and meansquare-error values outperforms ANN-based trivial models. The prediction model developed in this work is highly accurate with a Mean Absolute Percentage Error (MAPE) of approximately (3-6%) for all the sites.

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Heterogeneous Acceleration of Hybrid PSO-QN Algorithm for Neural Network Training

Cited by 9 publications

References 20 publications

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

Parallelization of Swarm Intelligence Algorithms: Literature Review

One-Month-Ahead Wind Speed Forecasting Using Hybrid AI Model for Coastal Locations

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