An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors

Yu, Lei; Li, Xiaoting; Liu, Jingsen; Ruan, Ximing

doi:10.3390/sym11070925

Cited by 38 publications

(31 citation statements)

References 51 publications

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“…However, the performance of the multitask network is sensitive to the weights,

, of task-specific losses. Generally, previous methods tuning weights of task-specific losses are usually grid search and heuristic technologies [ 26 , 33 ]. These kind of methods are extremely time consuming, which often take many days to complete a training trial.…”

Section: Methodsmentioning

confidence: 99%

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An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals

Fan

Wang

Zhao

et al. 2021

Sensors

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Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of 0.12±10.83 mmHg, 0.13±5.90 mmHg, and 0.08±6.47 mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension.

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“…However, the performance of the multitask network is sensitive to the weights,

Section: Methodsmentioning

confidence: 99%

“…However, the BP estimation performance of the proposed multitask is sensitive to the importance of task-specific losses. Traditionally, the optimal task-specific importance is searched by a grid search method or intelligent heuristic method [ 26 ]. However, both of them are time-consuming and high computing complexity.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals

Fan

Wang

Zhao

et al. 2021

Sensors

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“…In this section, to verify the searching performance of the SANGHS algorithm further, we used the SANGHS algorithm to solve two benchmark engineering problems and compared the experimental results with previous references [26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Two Benchmarks' Engineering Optimization Problemsmentioning

confidence: 99%

“…LAFBA [36] 0.18470619 3.64265569 9.13489736 0.20525405 1.72870000 GA1 [37] 0.24890000 6.17300000 8.17890000 0.25330000 2.43116000 RANDOM [38] 0.45750000 4.73130000 5.08530000 0.66000000 4.11855504 DAVID [38] 0.24340000 6.25520000 8.29150000 0.24440000 2.38410685 SIMPLEX [38] 0.27920000 5.62560000 7.75120000 0.27960000 2.53072583 APPOX [38] 0 Acknowledgments: The authors would like to thank all the reviewers for their constructive comments.…”

mentioning

confidence: 99%

An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance

Shih

Zhou

et al. 2020

Applied Sciences

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The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.

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“…Previously, several naturally or bio-inspired solutions are proposed to solve this critical issue and to improve the MPPT method for partial shading or inhomogeneous irradiation conditions [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. In more detail, research concentrates on various approaches such as bio-inspired algorithms [20], particle swarm optimization (PSO) algorithms [21][22][23][24], grey wolf optimization (GWO) technique [25], and simulated annealing (SA) based method [26]. Interestingly, one can obtain better results using hybrid evolutionary algorithms based on a combination of two or more different techniques.…”

Section: Introductionmentioning

confidence: 99%

An Approval of MPPT Based on PV Cell’s Simplified Equivalent Circuit During Fast-Shading Conditions

et al. 2019

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The partial shading conditions significantly affect the functionality of solar power plants despite the presence of multiple maximum power point tracking systems. The primary cause of this problem is the presence of local maxima in the power–current and/or power–voltage characteristic curves that restrict the functionality of the conventional maximum power point tracking systems. The present article proposes a modified algorithm based on the simplified equivalent circuit of solar cells to improve the functionality of traditional maximum power point tracking systems. This algorithm provides a method for regularly monitoring the photo-current of each solar module. The upper and lower boundaries of the regulating parameter such as current or voltage are decided very precisely, which is helpful to find the location of the global maximum. During a sequential search, the control system accurately determines the lower and upper boundaries of the global maximum. Simultaneously, the maximum power point tracking system increases the photovoltaic current up to one of these boundaries and applies one of the conventional algorithms. Additionally, the control system regularly monitors the photovoltaic characteristics and changes the limits of regulating parameter concerning any change in global maximum location. This proposed method is fast and precise to locate the global maximum boundaries and to track global maximum even under fast-changing partial shading conditions. The improved performance and overall efficiency are validated by simulation study for variable solar irradiance.

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An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors

Cited by 38 publications

References 51 publications

An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals

An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals

An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance

An Approval of MPPT Based on PV Cell’s Simplified Equivalent Circuit During Fast-Shading Conditions

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