Optimization of five-parameter BRDF model based on hybrid GA-PSO algorithm

Liu, Yuying; Dai, Jingjing; Zhao, Sisi; Zhang, Jinghao; Shang, Weidong; Li, Tong; Zheng, Yongchao; Lan, Tian; Wang, Zhiyong

doi:10.1016/j.ijleo.2020.164978

Cited by 30 publications

(9 citation statements)

References 5 publications

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“…Owing to the combination of MOPSO and GDM, MOPSO-GDM achieves best performance in feature selection compared with NSGA, MOPSO and another improved PSO: MOPSO-M algorithms, especially in high-dimensional space optimization ( Figure 7 ). From past studies, hybrid algorithms are commonly used as improved algorithms, such as GA-PSO (Gupta et al, 2019 ; Liu et al, 2020 ), BAPSO (Almadhor et al, 2021 ), PSO-GWO (Dahmani and Yebdri, 2020 ), and hybrid multi-objective optimization algorithms: NSGA-MOPSO (Shuaipeng et al, 2017 ; Xie et al, 2022 ). Nevertheless, most hybrid algorithms directly combined the whole of two algorithms, without considering the complexity of the algorithm (Wang et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Wang

Shi

et al. 2023

Front. Aging Neurosci.

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BackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.ObjectiveThis study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.MethodsFirst, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.ResultsFinally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

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Section: Discussionmentioning

confidence: 99%

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Wang

Shi

et al. 2023

Front. Aging Neurosci.

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“…In this study, to overcome the lack of exploitation ability in the genetic algorithm (GA), slow convergence, premature convergence and the tendency to fall into the local optimal solution in particle swarm optimization (PSO) [ 46 , 47 , 48 ], a novel combined method, AsyLnCPSO-GA, was presented and introduced to select the optimal feature combination, then fed to the naïve Bayesian classifier. Owing to the combination of AsyLnCPSO and GA, AsyLnCPSO-GA achieved best performance in feature selection compared with PSO and the improved PSO–AsyLnCPSO ( Figure 7 , Figure 8 , Figure 9 and Figure 10 ) algorithms.…”

Section: Discussionmentioning

confidence: 99%

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

Wang

Shi

et al. 2022

Entropy

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Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and am optimization algorithm for feature selection.

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“…PSO is a stochastic population-based algorithm that is easier to implement than GA, as this latter requires excessive effort to tune a large number of parameters. Additionally, PSO exploits a larger search space than GA [ 63 ]. Moreover, the particles in PSO have a memory term which is important for the algorithm.…”

Section: Algorithms For Facility Location In Vfcmentioning

confidence: 99%

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Hussain

Azar

Ahmed

et al. 2023

Sensors

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With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

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Optimization of five-parameter BRDF model based on hybrid GA-PSO algorithm

Cited by 30 publications

References 5 publications

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

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