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Aero-Engine Health Monitoring with Real Flight Data Using whale optimization algorithm Based Artificial Neural Network Technique

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OPTICAL MEMORY AND NEURAL NETWORKS 2021年第1期30卷 80-96页

作者： Balakrishnan, N. Devasigamani, Angello, I Anupama, K. R. Sharma, Nitin BITS Pilani Goa Campus Sancoale Goa India

Health monitoring of an aero-engine assumes importance in the light of primary requirements of flight safety and reliability. This paper proposes a novel, simple method for monitoring aircraft engine health using whale optimization algorithm based Artificial Neural Network (WOANN) technique, for analyzing the data downloaded from the health and usage monitoring system (HUMS) of a military aircraft. The actual engine data recorded during 47 different flights of eight different engines (of the same type) have been considered in this work. Thirteen engine parameters have been used to determine and monitor the health of the engine. The efficiency of the WOANN technique for engine health monitoring, is compared with that of three other common machine learning algorithms: Probabilistic based Neural Network (PNN), K-Nearest Neighbour (KNN), and Back propagation based Artificial Neural Network (BPANN). The results show that WOANN algorithm classifies and predicts engine health far more accurately as compared to PNN, KNN and BPANN. The values obtained for the metrics of Accuracy, Error, False Positive Rate, F1 score, Mathews Correlation Coefficient, Specificity, Kappa coefficient are found to be the best for WOANN algorithm. The WOANN achieved overall prediction accuracy of 95%, thus presenting itself as a very useful tool for day-to-day monitoring of aircraft engine health using the data downloaded from the aircraft's HUMS.

关键词： Health and Usage Monitoring Aero Engine whale optimization algorithm Artificial Neural Network Real Flight Data

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Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm

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RESOURCES POLICY 2019年 61卷 250-260页

作者： Alameer, Zakaria Abd Elaziz, Mohamed Ewees, Ahmed A. Ye, Haiwang Zhang Jianhua Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Hubei Peoples R China Al Azhar Univ Min & Petr Dept Fac Engn Qena Egypt Zagazig Univ Dept Math Fac Sci Zagazig Egypt Damietta Univ Dept Comp Dumyat Egypt

Developing an accurate forecasting model for long-term gold price fluctuations plays a vital role in future investments and decisions for mining projects and related companies. Viewed from this perspective, this paper proposes a novel model for accurately forecasting long-term monthly gold price fluctuations. This model uses a recent meta-heuristic method called whale optimization algorithm (WOA) as a trainer to learn the multilayer perceptron neural network (NN). The results of the proposed model are compared to other models, including the classic NN, particle swarm optimization for NN (PSO-NN), genetic algorithm for NN (GA-NN), and grey wolf optimization for NN (GWO-NN). Additionally, we employ ARIMA models as the benchmark for assessing the capacity of the proposed model. Empirical results indicate the superiority of the hybrid WOA NN model over other models. Moreover, the proposed WOA NN model demonstrates an improvement in the forecasting accuracy obtained from the classic NN, PSO-NN, GA-NN, GWO-NN, and ARIMA models by 41.25%, 24.19%, 25.40%, 25.40%, and 85.84% decrease in mean square error, respectively.

关键词： whale optimization algorithm Multilayer perceptron neural network Gold price fluctuations Forecasting Neural network

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Incorporating whale optimization algorithm with deep belief network for software development effort estimation

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第4期13卷 1637-1651页

作者： Kaushik, Anupama Singal, Niyati Prasad, Malvika Maharaja Surajmal Inst Technol Dept IT New Delhi India

The software industry is highly competitive, and hence, it is imperative to have an accurate method to estimate the effort needed in the key phases of software development. Accurate estimates ensure efficient allocation of human and machine resources for the project. This paper proposes a technique for software development effort estimation using deep belief network (DBN). For fine-tuning of DBN, whale optimization algorithm (WOA) is used which mimics the social behaviour of humpback whales. The proposed technique DBN-WOA has been experimentally evaluated on four promise datasets-COCOMO81, NASA93, MAXWELL and CHINA. The results from DBN-WOA are compared with the results from fine-tuning of DBN with backpropagation (DBN-BP) and it is observed that the proposed technique outscores DBN-BP. The proposed approach is also empirically validated through a statistical framework.

关键词： Software development effort estimation Deep belief network Backpropagation whale optimization algorithm Metaheuristics Neural networks

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Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm

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EARTH SCIENCE INFORMATICS 2020年第4期13卷 1351-1368页

作者： Youcefi, Mohamed Riad Hadjadj, Ahmed Bentriou, Abdelhak Boukredera, Farouk Said Univ Mhamed Bougara Boumerdes Fac Hydrocarbons & Chem Lab Petr Equipments Reliabil & Mat Ave Independance Boumerdes 35000 Algeria

Modeling the rate of penetration (ROP) plays a fundamental role in drilling optimization since the achievement of an optimum ROP can drastically reduce the overall cost of drilling activities. Evolved Extreme learning machine (ELM) with the evolutionary algorithms and multi-layer perceptron with Levenberg-Marquardt training algorithm (MLP-LMA) were proposed in this study to predict ROP. This paper focused mainly on two aspects. The first one was the investigation of the whale optimization algorithm (WOA) to optimize the weights and biases between input and hidden layers of ELM to enhance its prediction accuracy. The other was to adopt a prediction methodology that seeks to update the predictive model at each formation in order to reduce the dimension of input data and mitigate the effect of non real-time data such as the formation properties on the bit speed prediction. The prediction models were trained and tested using 3561 data points gathered from an Algerian field. The statistical and graphical evaluation criteria show that the ELM-WOA exhibited higher accuracy and generalization performance compared with the ELM-PSO and MLP-LMA. Furthermore, ELM-WOA was compared with two well-known ROP correlations in the literature, and the comparison results reveal that the proposed ELM-WOA model is superior to the pre-existing correlations. The findings of this study can help for the achievement of an optimum ROP and the reduction of the non-productive time. In addition, the outputs of this study can be used as an objective function during the real-time optimization of the drilling operation.

关键词： Rate of penetration Drilling optimization Extreme learning machine Multi-layer perceptron whale optimization algorithm Particle swarm optimization

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Apache Spark Implementation of whale optimization algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2020年第3期23卷 2021-2034页

作者： AlJame, Maryam Ahmad, Imtiaz Alfailakawi, Mohammad Kuwait Univ Dept Comp Engn Kuwait Kuwait

Population-based meta-heuristic algorithms are among the dominant algorithms used to solve challenging real world problems in diverse fields. whale optimization algorithm (WOA) is a recent swarm intelligence meta-heuristic algorithm based on the bubble-net feeding behavior of humpback whales. Despite its capability to solve complex optimization problems, WOA requires enormous amount of computations when solving large size problems. This work proposes Spark-WOA, a distributed implementation of WOA on Apache Spark platform to enhance its performance and reduce computational complexity. The proposed algorithm exploits in-memory computations and broadcast features of Apache Spark to provide better performance and scalability. Details of the proposed algorithm are presented and its performance as compared to a recent Apache Hadoop implementation is discussed. Experimental results demonstrated the superiority of the proposed implementation in terms of both speed and scalability.

关键词： Apache Spark Cluster Hadoop whale optimization algorithm

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Power system voltage instability risk mitigation via emergency demand response-based whale optimization algorithm

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Protection and Control of Modern Power Systems 2019年第1期4卷 295-308页

作者： Mohammed Amroune Tarek Bouktir Ismail Musirin Department of Electrical Engineering University of Ferhat Abbas Setif 119000SetifAlgeria Faculty of Electrical Engineering Universiti Teknologi MARA40450Shah AlamMalaysia

In recent years, due to the economic and environmental issues, modern power systems often operate proximately to the technical restraints enlarging the probable level of instability risks. Hence, efficient methods for voltage instability prevention are of great importance to power system companies to avoid the risk of large blackouts. In this paper, an event-driven emergency demand response (EEDR) strategy based on whale optimization algorithm (WOA) is proposed to effectively improve system voltage stability. The main objective of the proposed EEDR approach is to maintain voltage stability margin (VSM) in an acceptable range during emergency situations by driving the operating condition of the power system away from the insecure points. The optimal locations and amounts of load reductions have been determined using WOA algorithm. To test the feasibility and the efficiency of the proposed method, simulation studies are carried out on the IEEE 14-bus and real Algerian 114-bus power systems.

关键词： Voltage stability Emergency demand response whale optimization algorithm Blackouts

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Tuning of control parameters of the whale optimization algorithm using fuzzy inference system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期42卷 3051-3066页

作者： Krainski Ferrari, Allan Christian Gouvea da Silva, Carlos Alexandre Osinski, Cristiano Firmino Pelacini, Douglas Antonio Leandro, Gideon Villar Coelho, Leandro dos Santos Univ Fed Parana Dept Elect Engn Elect Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil

The whale optimization algorithm (WOA) is a recent approach to the swarm intelligence field that can be explored in many global optimization applications. This paper proposes a new mechanism to tune the control parameters that influence the hunting process in the WOA to improve its convergence rate. This schema adjustment is made by a fuzzy inference system that uses the normalized fitness value of each whale and the hunting mechanism control parameters of WOA. The method proposed was tested and compared with the conventional WOA and another version that uses a fuzzy inference system as input information on the ratio of the current iteration number and the maximum number of iterations. For performance analysis of the method proposed, all optimizers were evaluated with twenty-three benchmark optimization functions in the continuous domain. The algorithms were also implemented in the identification process of two real control system that are a boiler system and water supply network. For identification process, it is used the value of MSE (mean squared error) to available each algorithm. The simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional WOA and it is competitive in relation to another fuzzy version adopted in the WOA design.

关键词： Humpback whale Metaheuristics optimization identification process whale optimization algorithm

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A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年 1-14页

作者： Li, Mingyuan Yu, Xiaobing Fu, Bingbing Wang, Xuming Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China

whale optimization algorithm (WOA) is an outstanding nature-inspired algorithm widely used to solve many complex engineering optimization problems. However, WOA has a poor balance in exploration and exploitation, which converges to local optimum easily. This article proposes a Modified whale optimization algorithm (MWOA) with multi-strategy mechanism, which introduces the elite reverse learning strategy, nonlinear convergence factor, DE/rand/1 mutation strategy and Levy flight disturbance strategy. MWOA can improve the convergent ability and maintain the balance of exploitation and exploration to avoid local optimum. Compared with WOA, PSO, MFO, SOA, SCA and other four WOA variants on the CEC2017 benchmark suite, MWOA has strong competitiveness and can better improve the efficiency of WOA according to the experimental results and analysis.

关键词： whale optimization algorithm Multi-strategy Elite reverse learning strategy Nonlinear convergence factor

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Hybrid whale optimization algorithm with simulated annealing for load frequency controller design of hybrid power system

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SOFT COMPUTING 2023年 1-24页

作者： Nayak, Pratap Chandra Mishra, Sonalika Prusty, Ramesh Chandra Panda, Sidhartha VSSUT Dept Elect Engn Burla 768018 Odisha India

This research paper presents an innovative approach by hybridizing the whale optimization algorithm-WOA and the Simulated Annealing-SA, named as h-WOA-SA algorithm. The preeminence of the projected hybridized algorithm is evaluated over the basic form, modified form and some popular algorithms in respect of computation time and eminence in the tuning of fitness function by considering various standard test functions. Further, the hybrid algorithm is utilized for the scaling of interval type-2 fuzzy fractional order PID-IT2F-FO-PID controller engaged in load frequency control-LFC of hybrid power system-HPS. It presents a strategy to manage the critical challenges encountered due to the penetration of solar & wind power for their operational dynamics. The sturdiness of the hybrid technique based controller is examined in the HPS. The unrivalled superiority of the proposed approach has been validated by contrasting some recent research outcomes and with a real-time validation which justifies the effectiveness in improved performance as compared to the traditional approaches.

关键词： Hybrid power system whale optimization algorithm Simulation annealing Fractional order Type 2 fuzzy logic controller Renewable energy sources Frequency regulation

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Wind Power Interval Prediction Based on Improved whale optimization algorithm and Fast Learning Network

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第3期17卷 1785-1802页

作者： Zhang, Dewang Chen, Zhigeng Zhou, Yu HaiNan Normal Univ Coll Informat Sci & Technol Haikou 571158 Hainan Peoples R China HuaNeng HaiNan Clean Energy Branch Operat Dept Haikou 572000 Hainan Peoples R China

The uncertainty of wind power brings great challenges to large-scale wind power integration. The conventional point prediction of wind power is difficult to meet the demand of power grid planning and operation. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. An interval prediction model based on an improved whale optimization algorithm (IWOA) and fast learning network (FLN) was developed in this study. First the convergence speed and accuracy of the IWOA was enhanced by adjusting the nonlinear convergence factor, and by adding adaptive inertia weights and a chaos search strategy. Second, a novel evaluation index was proposed according to the lower and upper bound estimation method. The proposed evaluation index was considered as a fitness function, and the FLN parameters were optimized by the IWOA to output the final prediction interval. The examples considered in this study revealed that the proposed method can be employed to reduce the prediction interval normalized root-mean-square bandwidth (PINRW) and the prediction interval average deviation (PIAD) more than 3% and 8% at the 90% and 80% confidence level respectively, which has a high practical significance.

关键词： whale optimization algorithm Fast learning network Wind power Interval prediction

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