Hybridization of Constriction Coefficient-Based Particle Swarm Optimization and Chaotic Gravitational Search Algorithm for Solving Engineering Design Problems

Rather, Sajad Ahmad; Bala, P. Shanthi

doi:10.1007/978-981-15-3852-0_7

Cited by 19 publications

(13 citation statements)

References 18 publications

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“…where G(t) and G(t o ) are the final and initial values of G, α is a small constant, CI is the current iteration, and MI is the maximum number of iterations. The behaviour of G over time is proposed by Rather and Bala [87] using a chaotic normalisation process. Thus, the final description of the gravitational constant can be formulated as in Equation ( 16):…”

Section: A Constriction Coefficient-based Particle Swarm Optimisation...mentioning

confidence: 99%

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

et al. 2022

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Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively.

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Section: A Constriction Coefficient-based Particle Swarm Optimisation...mentioning

confidence: 99%

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

et al. 2022

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“…It has been seen that during the optimization process, the PSO particles move outside the solution space which results in the slow convergence of the candidate solutions towards feasible regions (Rather & Bala, 2019a, 2019b, 2019c, 2019d, 2020a, 2020b. To resolve the issue, constriction coefficients were introduced in PSO (Clerc & Kennedy, 2002) to accelerate the exploitation of the particles and therefore, increase the performance of the PSO.…”

Section: Constriction Coefficient Based Psomentioning

confidence: 99%

Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

Rather

Bala

2021

Expert Systems

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Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC-SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non-parametric signed Wilcoxon rank-sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.

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“…Additionally, particle swarm optimisation (PSO) has been employed in multiple hydrology areas, for example, WL [38] and streamfow [39]. Moreover, the constriction coefcient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) was proposed by Rather and Bala [40], and it is used in the prediction of drought [41]. Te CPSOCGSA algorithm was proposed under the strategy of hybridising the existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

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Hybridization of Constriction Coefficient-Based Particle Swarm Optimization and Chaotic Gravitational Search Algorithm for Solving Engineering Design Problems

Cited by 19 publications

References 18 publications

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

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