Particle swarm optimization based artificial neural network model for forecasting groundwater level in Udupi district

Balavalikar, Supreetha; Nayak, Padma L.; Shenoy, Narayan K; Nayak, Krishnamurthy

doi:10.1063/1.5031983

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…Subdividing the swarm can prevent stagnation but requires more function evaluations, which increases the algorithm's running time [22,34,37,38]. PSO also hybridizes with other algorithms to improve its results similar to Multi-gradient PSO in [24], Fuzzy Self-Tuning PSO in [19], PSO with artificial neural networks in [39], Global Genetic Learning PSO in [21], Multi-swarm PSO with evolutionary algorithms [40], Multi-chaotic PSO with deterministic chaos and evolutionary computation techniques [18], and PSO with reinforced learning and a multi-swarm approach [17].…”

Section: Introductionmentioning

confidence: 99%

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Rivera,

Guerrero-Mendez,

Lopez-Betancur

et al. 2023

Mathematics

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Optimizing large-scale numerical problems is a significant challenge with numerous real-world applications. The optimization process is complex due to the multi-dimensional search spaces and possesses several locally optimal regions. In response to this issue, various metaheuristic algorithms and variations have been developed, including evolutionary and swarm intelligence algorithms and hybrids of different artificial intelligence techniques. Previous studies have shown that swarm intelligence algorithms like PSO perform poorly in high-dimensional spaces, even with algorithms focused on reducing the search space. However, we propose a modified version of the PSO algorithm called Dynamical Sphere Regrouping PSO (DSRegPSO) to avoid stagnation in local optimal regions. DSRegPSO is based on the PSO algorithm and modifies inertial behavior with a regrouping dynamical sphere mechanism and a momentum conservation physics effect. These behaviors maintain the swarm’s diversity and regulate the exploration and exploitation of the search space while avoiding stagnation in optimal local regions. The DSRegPSO mechanisms mimic the behavior of birds, moving particles similar to birds when they look for a new food source. Additionally, the momentum conservation effect mimics how birds react to collisions with the boundaries in their search space or when they are looking for food. We evaluated DSRegPSO by testing 15 optimizing functions with up to 1000 dimensions of the CEC’13 benchmark, a standard for evaluating Large-Scale Global Optimization used in Congress on Evolutionary Computation, and several journals. Our proposal improves the behavior of all variants of PSO registered in the toolkit of comparison for CEC’13 and obtains the best result in the non-separable functions against all the algorithms.

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Section: Introductionmentioning

confidence: 99%

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Rivera,

Guerrero-Mendez,

Lopez-Betancur

et al. 2023

Mathematics

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“…Inspired by the artificial neural network (ANN) , and principal component analysis (PCA), − Lou et al proposed a nonlinear approach called neural component analysis (NCA), which reconstructs the ANN with PCA principles, adopts a neural network structure for nonlinearity description, and updates the parameters by gradient descent . As such, NCA provides a new idea for solving the nonlinear monitoring issue, which can be transplanted to other algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, these parameters should be tuned by trial and error, and the nonlinear mapping model is not optimal. 24 Inspired by the artificial neural network (ANN) 25,26 and principal component analysis (PCA), 27−29 30 which reconstructs the ANN with PCA principles, adopts a neural network structure for nonlinearity description, and updates the parameters by gradient descent. 31 NCA provides a new idea for solving the nonlinear monitoring issue, which can be transplanted to other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Neural Component Analysis for Key Performance Indicator Monitoring

Wang

Hou

et al. 2022

ACS Omega

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The partial least squares (PLS) algorithm is a commonly used key performance indicator (KPI)-related performance monitoring method. To address nonlinear features in the process, this paper proposes neural component analysis (NCA)-PLS, which combines PLS with NCA. (NCA)-PLS realizes all the principles of PLS by introducing a new loss function and a new principal component selection mechanism to NCA. Then, the gradient descent formulas for network training are rederived. NCA-PLS can extract components with large correlations with KPI variables and adopt them for data reconstruction. Simulation tests using a mathematical model and the Tennessee Eastman process show that NCA-PLS can successfully handle nonlinear relationships in process data and that it performs much better than PLS, KPLS, and NCA.

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“…Artificial neural network (ANN), − which is inspired by the structure and function of biological neural networks in the brain, is a successful technology for handling the nonlinearity in data and is used in a large variety of applications in various areas. ANN describes the nonlinearity using activation function and learns the nonlinear relationships among data by adjusting relevant weights and bias parameters, so ANN can obtain an optimal nonlinear model for the nonlinear process.…”

Section: Introductionmentioning

confidence: 99%

New Nonlinear Approach for Process Monitoring: Neural Component Analysis

Lou

Wang

2020

Ind. Eng. Chem. Res.

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Nonlinearity is extremely common in industrial processes. For handling the nonlinearity problem, this paper combines artificial neural networks (ANN) with principal component analysis (PCA) and proposes a new neural component analysis (NCA). NCA has a similar network structure as ANN and adopts the gradient descent method for training, hence it has the same nonlinear fitting ability as ANN. Furthermore, NCA adopts PCA's dimension reduction strategy to extract the uncorrelated components from the process data and constructs statistical indices for process monitoring. The simulation test results show that NCA can successfully extract the uncorrelated components from the nonlinear process data, and it has better performance than other nonlinear approaches.

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Particle swarm optimization based artificial neural network model for forecasting groundwater level in Udupi district

Cited by 14 publications

References 10 publications

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Neural Component Analysis for Key Performance Indicator Monitoring

New Nonlinear Approach for Process Monitoring: Neural Component Analysis

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