scholarly journals An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1316
Author(s):  
Kanhua Yu ◽  
Lili Liu ◽  
Zhe Chen
Keyword(s):  
Water Consumption ◽  
Prediction Accuracy ◽  
Learning Strategy ◽  
Model Parameters ◽  
Slime Mould ◽  
Opposition Based Learning ◽  
Searching Strategy ◽  
Practical Engineering ◽  
Benchmark Test Functions ◽  
Estimation Models

A slime mould algorithm (SMA) is a new meta-heuristic algorithm, which can be widely used in practical engineering problems. In this paper, an improved slime mould algorithm (ESMA) is proposed to estimate the water demand of Nanchang City. Firstly, the opposition-based learning strategy and elite chaotic searching strategy are used to improve the SMA. By comparing the ESMA with other intelligent optimization algorithms in 23 benchmark test functions, it is verified that the ESMA has the advantages of fast convergence, high convergence precision, and strong robustness. Secondly, based on the data of historical water consumption and local economic structure of Nanchang, four estimation models, including linear, exponential, logarithmic, and hybrid, are established. The experiment takes the water consumption of Nanchang City from 2004 to 2019 as an example to analyze, and the estimation models are optimized using the ESMA to determine the model parameters, then the estimation models are tested. The simulation results show that all four models can obtain better prediction accuracy, and the proposed ESMA has the best effect on the hybrid prediction model, and the prediction accuracy is up to 97.705%. Finally, the water consumption of Nanchang in 2020–2024 is forecasted.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

SIMULATION OF THE ALCOHOL-OIL MIXTURE IN A T-SHAPED MICROCHANNEL USING THE DISSIPATIVE PARTICLE DYNAMICS METHOD ON GPU DEVICES

2014 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
A. A. Horta ◽  
L. O. S. Ferreira ◽  
E. L. Martinez ◽  
R. Maciel Filho
Keyword(s):  
Dissipative Particle Dynamics ◽  
Fluid Motion ◽  
Particle Dynamics ◽  
Immiscible Fluids ◽  
Biodiesel Production ◽  
Model Parameters ◽  
Processing Unit ◽  
Fluid Properties ◽  
Practical Engineering ◽  
Multiphase Fluid

Multiphase fluid motion in microchannnels involves complicated fluid dynamics and is fundamentally important to diverse practical engineering applications. Among several applications, the alcohol-oil mixture is particularly important due to its application for biodiesel production. In this work, the mixture of immiscible fluids alcohol-oil in a square T-shaped microchannel was investigated using the Dissipative Particle Dynamics (DPD) method available in the HOOMD simulator, which runs on a single graphic processing unit (GPU). The immiscible fluids were achieved by increasing the repulsive force between species. The fluid properties and hydrodynamic behavior were discussed in function of model parameters. The simulation results agree with data published in the literature showing that the DPD is appropriate for simulation of mass transport on complex geometries in microscale on a single GPU.

scholarly journals GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Tomáš Brůna ◽  
Alexandre Lomsadze ◽  
Mark Borodovsky
Keyword(s):  
Prediction Accuracy ◽  
Gene Prediction ◽  
External Information ◽  
Model Parameters ◽  
Protein Database ◽  
Sequencing Project ◽  
Automated Method ◽  
Eukaryotic Gene ◽  
Estimation Of Model Parameters ◽  
Eukaryotic Genomes

Abstract We have made several steps toward creating a fast and accurate algorithm for gene prediction in eukaryotic genomes. First, we introduced an automated method for efficient ab initio gene finding, GeneMark-ES, with parameters trained in iterative unsupervised mode. Next, in GeneMark-ET we proposed a method of integration of unsupervised training with information on intron positions revealed by mapping short RNA reads. Now we describe GeneMark-EP, a tool that utilizes another source of external information, a protein database, readily available prior to the start of a sequencing project. A new specialized pipeline, ProtHint, initiates massive protein mapping to genome and extracts hints to splice sites and translation start and stop sites of potential genes. GeneMark-EP uses the hints to improve estimation of model parameters as well as to adjust coordinates of predicted genes if they disagree with the most reliable hints (the -EP+ mode). Tests of GeneMark-EP and -EP+ demonstrated improvements in gene prediction accuracy in comparison with GeneMark-ES, while the GeneMark-EP+ showed higher accuracy than GeneMark-ET. We have observed that the most pronounced improvements in gene prediction accuracy happened in large eukaryotic genomes.

scholarly journals A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2160 ◽  
Author(s):  
Xiaomin Wu ◽  
Weihua Cao ◽  
Dianhong Wang ◽  
Min Ding
Keyword(s):  
Power Loss ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Inertial Mass ◽  
Real Time Scheduling ◽  
Opposition Based Learning ◽  
Hybrid Particle Swarm Optimization ◽  
Time Scheduling ◽  
Benchmark Test Functions ◽  
Energy Conversion Devices

With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

scholarly journals Multi-Objective Interval Prediction of Load Based on the Conditional Copula Function

2019 ◽  
Vol 9 (5) ◽  
pp. 955 ◽  
Author(s):  
Gang Zhang ◽  
Zhixuan Li ◽  
Jinwang Hou ◽  
Kaoshe Zhang ◽  
Fuchao Liu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Interval ◽  
Prediction Method ◽  
Copula Function ◽  
Solution Set ◽  
Shaanxi Province ◽  
Model Parameters ◽  
Multi Objective ◽  
Interval Prediction ◽  
Conditional Copula

Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.

scholarly journals Adaptive Opposition Slime Mould Algorithm

2021 ◽  
Author(s):  
Manoj Kumar Naik ◽  
Rutuparna Panda ◽  
Ajith Abraham
Keyword(s):  
Random Search ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Function Optimization ◽  
Rank Test ◽  
Test Functions ◽  
Slime Mould ◽  
Opposition Based Learning ◽  
Suggested Technique ◽  
Better Than

Abstract Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, the SMA uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, we investigate an adaptive approach to decide whether opposition based learning (OBL) will be used or not. Sometimes the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The qualitative and quantitative analysis of AOSMA is reported using 29 test functions consisting of 23 classical test functions and 6 recently used composition functions from the IEEE CEC 2014 test suite. The results are compared with state-of-the-art optimization methods. Results presented in this paper show that AOSMA’s performance is better than other optimization algorithms. The AOSMA is evaluated using Wilcoxon’s rank-sum test. It also ranked one in Friedman’s mean rank test. The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems.

scholarly journals An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1551
Author(s):  
Shuang Wang ◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
Rong Zheng
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Energy Parameter ◽  
Industrial Engineering ◽  
Superior Performance ◽  
Exploration And Exploitation ◽  
Design Problems ◽  
Local Optima ◽  
Opposition Based Learning ◽  
Engineering Design Problems

Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO is pretty good, while the exploration capability is far from satisfactory. Considering the characteristics of these two algorithms, an improved hybrid AO and HHO combined with a nonlinear escaping energy parameter and random opposition-based learning strategy is proposed, namely IHAOHHO, to improve the searching performance in this paper. Firstly, combining the salient features of AO and HHO retains valuable exploration and exploitation capabilities. In the second place, random opposition-based learning (ROBL) is added in the exploitation phase to improve local optima avoidance. Finally, the nonlinear escaping energy parameter is utilized better to balance the exploration and exploitation phases of IHAOHHO. These two strategies effectively enhance the exploration and exploitation of the proposed algorithm. To verify the optimization performance, IHAOHHO is comprehensively analyzed on 23 standard benchmark functions. Moreover, the practicability of IHAOHHO is also highlighted by four industrial engineering design problems. Compared with the original AO and HHO and five state-of-the-art algorithms, the results show that IHAOHHO has strong superior performance and promising prospects.

scholarly journals A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongli Liu ◽  
Zhonghui Wang ◽  
Hao Chao
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Opposition Based Learning ◽  
Fuzzy Clustering Algorithm ◽  
The Impact

Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness.

scholarly journals A Novel Parametric Model for Nonlinear Hysteretic Behaviors with Strain Stiffening of Magnetorheological Gel Based on Fruit Fly Optimization Algorithm

2021 ◽  
Author(s):  
Guang Zhang ◽  
Zheng Zhang ◽  
Min Sun ◽  
Yang Yu ◽  
Jiong Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Parametric Model ◽  
Measure Data ◽  
Model Parameters ◽  
Fruit Fly Optimization Algorithm ◽  
Engineering Applications ◽  
Fruit Fly Optimization ◽  
Practical Engineering

Abstract Magnetorheological (MR) gel is a new branch of MR materials, which can overcome the phenomenon of particle agglomeration existing in MR fluid, thus improving the controllability of materials in engineering applications. In this paper, a novel parametric model for tracking the nonlinear hysteretic behaviors with strain stiffening of MR gel is constructed. The measure data in relative to the five current levels of 0A, 0.2A, 0.5A, 0.8A and 1A under the strain amplitude and frequency of 10% and 0.1Hz respectively are utilized to identify the parameters. The optimal solution for the model parameters is conduced employing the fruit fly optimization algorithm (FOA). The comparison study with two typical model such as Bouc-Wen model and viscoelastic-plastic model is conduced to to evaluate the effectiveness of the developed model. The model parameters are generalized with respect to the loading current, and the reliability of the generalized model is verified. The studies show that the proposed model can perfectly fit the strain stiffening and nonlinearity of sample, which can provide a theoretical basis for the semi-active control of MR gel in practical engineering applications.

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