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Neural Network Predictive Control of a Vapor Compression Cycle

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第2期45卷 779-796页

作者： Turgut, Mert Sinan Coban, Mustafa Turhan Ege Univ Mech Engn Dept Fac Engn TR-35040 Izmir Turkey

This study investigates the Neural Network Predictive Control of a vapor compression cycle (VCC). VCC consists of four components, namely the compressor, electronic expansion valve (EEV), evaporator and condenser. Modeling of the compressor and EEV is carried out with the static relationships, while modeling of the evaporator and condenser is performed with the lumped parameter moving boundary method. The established thermodynamic model is validated against the ASPEN model with the same design specifications. The neural network is trained off-line with the input and output signal data of the established model. The solution of the optimization problem for the each time step is achieved with the metaheuristic method called whale optimization algorithm in the predictive controller. Ultimately, performances of the four different controllers, namely the cooling load, first law efficiency, entropy generation and second law efficiency, are compared with each other. The results show that the entropy generation controller achieves the most favorable exergy destruction performance with 0.2% lower than the worst performer cooling load controller. It is also observed that the second law efficiency controller is the best performer in terms of the overall second law efficiency through the simulation time.

关键词： Vapor compression cycle Control systems whale optimization algorithm Neural Network Predictive Control Dynamic system modeling

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Combined heat and power economic dispatch problem solution by implementation of whale optimization method

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NEURAL COMPUTING & APPLICATIONS 2019年第2期31卷 421-436页

作者： Nazari-Heris, M. Mehdinejad, M. Mohammadi-Ivatloo, B. Babamalek-Gharehpetian, Gevork Univ Tabriz Smart Energy Syst Lab Fac Elect & Comp Engn Tabriz Iran Amirkabir Univ Technol Elect Engn Dept Tehran Iran

Combined heat and power economic dispatch (CHPED) is introduced as a difficult optimization problem, which provides optimal generation scheduling of heat and power units. The CHPED problem aims to minimize the fuel cost of CHP units with the consideration of operational constraints. In this paper, whale optimization algorithm (WOA) is employed to solve CHPED problem. WOA is a new meta-heuristic optimization technique, which is introduced recently for solving optimization problems. Social behavior of humpback whales is the basic idea of proposal of WOA, where the bubble-net hunting strategy inspires this optimization procedure. Three test systems are considered for evaluation of the performance of the WOA in solving the non-convex nonlinear CHPED problem. The first test instance, which includes 24 units, is studied for evaluating WOA performance in finding the optimal solution of CHPED problem. 84-Unit and 96-unit test systems are introduced for the first time in this paper to show the superiority of WOA in solving non-convex CHPED optimization problem. The second test system contains 40 power-only units, 24 CHP units, and 20 heat-only units. Additionally, the third test instance contains 52 power-only units, 24 CHP units, and 20 heat-only units. The obtained optimal solutions represent WOA efficiency and feasibility and capability of obtaining better solutions with respect to other optimization techniques in terms of operational cost and ability of implementation of WOA on large systems.

关键词： Combined heat and power Economic dispatch whale optimization algorithm Heuristic optimization Non-convex optimization

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WOA-Based Echo State Network for Chaotic Time Series Prediction

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JOURNAL OF THE KOREAN PHYSICAL SOCIETY 2020年第5期76卷 384-391页

作者： Zhang, Minghui Wang, Baozhu Zhou, Yatong Sun, Haoxuan Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

We present a new chaotic times prediction model inspired by the bubble-net predation of whales. The echo state network (ESN) is a new type of recurrent neural network. However, selecting parameters empirically for the ESN cannot guarantee the accuracy of the prediction. The whale optimization algorithm (WOA) imitates the bubble-net predation of whales and ensures the rapid convergence of selecting network parameters. A new prediction model, WOA-ESN, in which the WOA and the ESN are incorporated, is proposed in this paper. In addition, a simplified cross-validation (CV) method is proposed to take into account the approximation performance and generalization ability of the WOA-ESN. In experiments, the WOA-ESN is used for Mackey-Glass and Lorenz chaotic time series predictions, and the results are compared with the ESN based on particle swarm optimization (PSO-ESN), the ESN based on genetic algorithm (GA-ESN), and ESN. The results show that the proposed model has the best prediction performance.

关键词： Echo state network whale optimization algorithm Time series prediction Simplified cross-validation

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Modeling and simulation of an optimal unified tank model for aeration-sedimentation processes in wastewater treatment plants

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JOURNAL OF WATER PROCESS ENGINEERING 2020年 37卷

作者： Kus, Bayram Arda Kara, Tolgay Gaziantep Univ Dept Elect & Elect Engn TR-27310 Gaziantep Turkey

The operation of Wastewater Treatment Plants (WWTPs) depend not only on the influent characteristics and air blown into the tank, but also the design of the tank structure. In this paper, the mathematical model of the aeration tank is derived by the dynamic equations of physical and biological processes. It is aimed to obtain an effective alternative model for secondary treatment. In the conventional approach, two separate tanks are used for aeration and sedimentation processes. A Unified Tank Model (UTM) for both phases is proposed and investigated in this research. To reach the desired optimized solution, a recently introduced metaheuristic method based on whale feeding behavior, named as whale optimization algorithm (WOA) is used. The dynamic model of real system behavior in the proposed conditions is observed by computer simulation. The results are examined and discussed in the cases of the conventional approach and proposed alternative model. The optimization problems based on aeration system and dissolved oxygen (DO) concentration are defined to find optimal location of the diffusers in tank which is solved using whale optimization method. The optimal design shows better DO performance compared to the conventional approach. The aeration duration and accuracy of DO variation in the proposed UTM design with WOA optimization are shown to be significantly improved. The results of optimal WOA solution in UTM design present a useful alternative with good accuracy in following the desired DO value.

关键词： Process modeling Process simulation Wastewater treatment optimization whale optimization algorithm

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Inverse Problem Estimation of Heat Conduction using Parameters Adaptive PID algorithm

Inverse Problem Estimation of Heat Conduction using Paramete...

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第34届中国控制与决策会议

作者： Huang Weichao Li Jiahao Liu Ding Faculty of automation and Information Engineering Xian University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

A boundary inverse problem estimation method for heat conduction system based on a parameters adaptive PID algorithm is proposed in the *** method can solve the boundary heat flux estimation problem of onedimensional heat conduction model based on the inverse heat conduction *** reduce the error between the temperatures of the measurement points and the calculation values constantly by using feedback control of PID *** parameters of PID algorithm is optimized by whale optimization *** is enhances the rapidity and stability of the system and solves the problem that PID parameters are difficult to *** experimental results show that,the method that proposed in the paper can realize the inverse estimation of thermal boundary conditions accurately and quickly while ensuring the stability and convergence of the system.

关键词： Inverse heat conduction problem PID algorithm Finite difference method whale optimization algorithm

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An integrated multi-objective whale optimized support vector machine and local texture feature model for severity prediction in subjects with cardiovascular disorder

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2020年第4期15卷 601-615页

作者： Muthulakshmi, M. Kavitha, G. Anna Univ Dept Elect Engn MIT Campus Chennai 600044 Tamil Nadu India

Purpose The left ventricle (LV) myocardium undergoes deterioration with the reduction in ejection fraction (EF). The analysis of its texture pattern plays a major role in diagnosis of heart muscle disease severity. Hence, a classification framework with co-occurrence of local ternary pattern feature (COALTP) and whale optimization algorithm has been attempted to improve the prediction accuracy of disease severity level. Methods This analysis is carried out on 600 slices of 76 participants from Kaggle challenge that include subjects with normal and reduced EF. The myocardium of LV is segmented using optimized edge-based local Gaussian distribution energy (LGE)-based level set, and end-diastolic and end-systolic volumes were calculated. COALTP is extracted for two distance levels (d = 1 and 2). The t-test has been performed between the features of individual binary classes. The features are ranked using feature ranking methods. The experiments have been performed to analyze the performance of various percentages of features in each combination of bin for fivefold cross-validation. An integrated whale optimized feature selection and multi-classification framework is developed to classify the normal and pathological subjects using CMR images, and DeLong test has been performed to compare the ROCs. Results The optimized edge embedded to level set has produced better segmented myocardium that correlates with R = 0.98 with gold standard volume. The t-test shows that texture features extracted from severe subjects with distance level "1" are more statistically significant with a p value (< 0.00004) compared to other pathologies. This approach has produced an overall multi-class accuracy of 75% [confidence interval (CI) 63.74-84.23%] and effective subclass specificity of 70% (CI 55.90-81.22%). Conclusion The obtained results show that the multi-objective whale optimized multi-class support vector machine framework can effectively discriminate the healthy and patients with re

关键词： Heart muscle disease Magnetic resonance images Support vector machine whale optimization algorithm Local ternary pattern Optimized edge-based level set

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Parameter identification of fractional-order chaotic systems using different Meta-heuristic optimization algorithms

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NONLINEAR DYNAMICS 2019年第3期95卷 2491-2542页

作者： Yousri, D. A. AbdelAty, Amr M. Said, Lobna A. Elwakil, A. S. Maundy, Brent Radwan, Ahmed G. Fayoum Univ Dept Elect Engn Fac Engn Al Fayyum Egypt Fayoum Univ Engn Math & Phys Dept Fac Engn Al Fayyum Egypt Nile Univ NISC Giza Egypt Univ Sharjah Dept Elect & Comp Engn POB 27272 Sharjah U Arab Emirates Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada Cairo Univ Engn Math & Phys Dept Fac Engn Giza Egypt

Fractional-order chaotic systems (FOCS) parameter identification is an essential issue in chaos control and synchronization process. In this paper, different recent Meta-heuristic optimization algorithms are used to estimate the parameters and orders of three FOCS. The investigated systems are Arneodo, Borah rotational attractor and Chen double- and four-wing systems. The employed algorithms are the Salp Swarm algorithm, whale optimization algorithm, Moth-Flame Optimizer, Grey Wolf Optimizer and the Flower Pollination algorithm (FPA). The proposed algorithms are applied on several objective functions to identify the FOCS parameters including Mean Square Error (MSE), Integral of Squared Error (ISE), Integral of Absolute Error and Integral of Time Absolute Error. A comparison between the obtained results from each algorithm over each employed objective function is carried out. The target is to investigate the most adequate optimization technique in this difficult multidimensional problem and the best objective function that helps the algorithms capture more accurate and consistent results. The performance of optimization algorithms in the presence of measurement noise has been tested using two objective functions (MSE and ISE) for the three chaotic systems. The overall outcome shows that FPA with ISE objective function is the most efficient combination for the parameter identification of the three FOCS without/with noise because it achieves higher accuracy and more robust results with faster convergence speeds than all other algorithms.

关键词： Fractional-order chaotic systems Salp Swarm algorithm whale optimization algorithm Moth-Flame Optimizer Grey Wolf Optimizer Flower Pollination algorithm IAE ISE ITAE MSE

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W-Scheduler: whale optimization for task scheduling in cloud computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 1087-1098页

作者： Sreenu, Karnam Sreelatha, M. Acharya Nagarjuna Univ Dept Comp Sci & Engn ANU Coll Engn Guntur Andhra Pradesh India RVR & JC Coll Engn Dept Comp Sci & Engn Guntur Andhra Pradesh India

One of the important steps in cloud computing is the task scheduling. The task scheduling process needs to schedule the tasks to the virtual machines while reducing the makespan and the cost. Number of scheduling algorithms are proposed by various researchers for scheduling the tasks in cloud computing environments. This paper proposes the task scheduling algorithm called W-Scheduler based on the multi-objective model and the whale optimization algorithm (WOA). Initially, the multi-objective model calculates the fitness value by calculating the cost function of the central processing unit (CPU) and the memory. The fitness value is calculated by adding the makespan and the budget cost function. The proposed task scheduling algorithm with the whale optimization algorithm can optimally schedule the tasks to the virtual machines while maintaining the minimum makespan and cost. Finally, we analyze the performance of the proposed W-Scheduler with the existing methods, such as PBACO, SLPSO-SA, and SPSO-SA for the evaluation metrics makespan and cost. From the experimental results, we conclude that the proposed W-Scheduler can optimally schedule the tasks to the virtual machines while having the minimum makespan of 7 and minimum average cost of 5.8.

关键词： Cloud computing Task scheduling Multi-objective model whale optimization algorithm Makespan

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Estimation of the Smoothing Parameter in Probabilistic Neural Network Using Evolutionary algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第4期45卷 2945-2955页

作者： Naik, Shraddha M. Jagannath, Ravi Prasad K. Kuppili, Venkatanareshbabu Natl Inst Technol Goa Dept Comp Sci & Engn Farmagudi Goa India Natl Inst Technol Goa Dept Appl Sci Farmagudi Goa India

The probabilistic neural network (PNN) is an efficient approach that can compute nonlinear decision boundaries, widely used for classification. In this paper, the often used Gaussian distribution function is replaced by a new probability density function which provides a new variant of the PNN method. Most of the higher-dimensional data are statistically found to be not from the normal distribution, and hence, we have replaced it by the symmetric Laplace distribution. Further, the estimation of the smoothing parameter in the proposed PNN model is carried out with three different evolutionary algorithms, namely bat algorithm (BA), grey wolf optimizer (GWO), and whale optimization algorithm (WOA) with a novel fitness function. These different proposed PNN models with variable smoothing parameter estimation methods are tested on five different benchmark data sets. The performance of proposed three Laplace distribution-based variants of PNN incorporated with BA, GWO, and WOA are reported and compared with Gaussian-based variants of PNN and also other commonly used classifiers: the conventional PNN, extreme learning machine, and K-nearest neighbor in terms of measurement accuracy. The results demonstrate that the proposed approaches using evolutionary algorithms can provide as much as a ten percent increase in accuracy over the conventional PNN method.

关键词： Bat algorithm Classification Grey wolf optimization Probabilistic neural network whale optimization algorithm

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Investigation of a New Hybrid optimization algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems

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WATER RESOURCES MANAGEMENT 2019年第14期33卷 4767-4782页

作者： Mohammadi, Majid Farzin, Saeed Mousavi, Sayed-Farhad Karami, Hojat Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran

Reservoirs' optimal operation is a critical issue in the management of surface water resources. In the present study, after combining the whale optimization algorithm (WOA) with genetic algorithm (GA), which is called hybrid whale-genetic algorithm (HWGA), the precision and convergence rate of HWGA is evaluated in the optimal operation of continuous-time four-reservoir benchmark system (FRBS) and ten-reservoir benchmark system (TRBS). This combination benefits from the GA high precision and the WOA high convergence rate. The precision and convergence rate of HWGA, GA, and WOA are compared to the absolute optimum solution, obtained using Lingo software. Results indicated that the absolute optimal solution was 308.292 in the FRBS and 1194.441 in the TRBS. The best optimal solutions using the HWGA, GA and WOA were 96.08%, 95.76%, and 85.19% of the absolute optimal in the FRBS, respectively, and 97.24%, 89.54%, and 84.42% of the absolute optimal in the TRBS, respectively. So, the precision of HWGA, GA and WOA ranked first to third, respectively. Also, the variation coefficient of HWGA solutions (0.006 in the FRBS and 0.011 in the TRBS) had the lowest value in both benchmark systems. The variation coefficients of GA and WOA solutions were 1.24 and 3.57 times the variation coefficient of HWGA in the FRBS, respectively, and 1.55 and 5.32 times the variation coefficient of HWGA in the TRBS, respectively. Therefore, it could be concluded that in the current study the HWGA solutions variation range was narrower than other algorithms' solutions. According to four criteria of the objective function average, standard deviation, number of population, and maximum number of iterations, the performance of the algorithms in the present study is compared to the performance of some algorithms in other literatures using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of TOPSIS indicated that HWGA ranked first and WOA ranked last in both benchmark

关键词： Reservoir operation Benchmark systems whale optimization algorithm Genetic algorithm

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