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Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第19期235卷 4458-4474页

作者： Mo, Zhenling Zhang, Heng Wang, Jinglin Wang, Jianyu Fu, Hongyong Miao, Qiang Sichuan Univ Sch Aeronaut & Astronaut Ctr Aerosp Informat Proc & Applicat Chengdu Sichuan Peoples R China Aviat Key Lab Sci & Technol Fault Diag & Hlth Man Shanghai Peoples R China Technol & Engn Ctr Space Utilizat Chinese Acad Sci Key Lab Space Utilizat Beijing Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610065 Sichuan Peoples R China

Meyer wavelet filters are the key building blocks of empirical wavelet transform. In mechanical fault diagnosis, however, the boundaries of Meyer wavelet filters are usually defined empirically. In order to solve the problems, this paper proposes a new index called harmonic infinite-taxicab norm to guide grasshopper optimization algorithm to primarily optimize a band-pass filter and thus, concurrently and secondarily optimize a low-pass filter and a high-pass filter of Meyer wavelet. The proposed index is inspired by spectral Lp/Lq norm and it is closely related to fault characteristic frequency of rotating machinery. In addition, only three Meyer wavelet filters are demanded in each iteration of optimization. The effectiveness of the proposed method is validated by comparing with fast kurtogram method on analyzing faulty bearing data and gearbox data.

关键词： Wavelet filter general Lp norm optimization algorithm rotating machinery fault diagnosis

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Black-Box Function Aerodynamic Topology optimization algorithm via Machine Learning Technologies

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AIAA JOURNAL 2021年第12期59卷 5174-5185页

作者： Ban, Naohiko Yamazaki, Wataru Nagaoka Univ Technol Dept Energy & Environm Sci Niigata 9402188 Japan Nagaoka Univ Technol Dept Mech Engn Niigata 9402188 Japan

The objective of this research is to construct an efficient global topology optimization method using machine learning technologies. In the conventional design process of mechanical design, the conceptual design is the earliest stage of the design process;and it is carried out based on the designer's own idea/experience. Therefore, it is difficult to obtain innovative and high-quality concepts overwhelming the designer's knowledge since the earliest stage of the design process has the largest impact. In this research, therefore, the black-box function aerodynamic topology optimization algorithm via machine learning technologies (FANTOM) is developed to overcome the problem. In the FANTOM approach, topology optimization problems are solved using/combining two efficient global optimization methods developed by the authors: the efficient global optimization method for discontinuous optimization problems with infeasible regions using classification method, and the efficient global optimization method via clustering/classification methods and exploration strategy. In the present approach, topological optimal designs can be obtained only by setting an objective function and constraint conditions. The validity of the FANTOM approach is demonstrated in an inviscid drag minimization problem at a two-dimensional supersonic flow condition, which provides an optimal topology as the Busemann biplane airfoil. Executing topology optimizations with the variation in freestream Mach number, it is also demonstrated that the FANTOM approach can explore topological optimal designs robustly.

关键词： optimization algorithm Support Vector Machine Black Box Aerodynamic Shape optimization Topology optimization Supersonic Airfoils Supersonic Flow Numerical Simulation Aerodynamic Performance Surrogate Model

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A new approach for solving the flow-shop scheduling problem using a parallel optimization algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第12期12卷 10723-10732页

作者： Nazif, Habibeh Payame Noor Univ PNU Dept Math POB 19395-4697 Tehran Iran

In any organization and business, efficient scheduling cause increased efficiency, reducing the time required to complete jobs and increasing an organization's profitability in a competitive environment. Also, the flow-shop scheduling problem is a vital type of scheduling problem with many real-world applications. Flow-shop scheduling has numerous exciting applications in various manufacturing and industrial domains. During the past eras, the growing interests in the arrangement of flow shops with diverse objective functions (for example, minimizing the makespan and flow-time) were observed. The permutation flow-shop is formulated as mixed-integer programming, and it is an NP-Hard problem. Therefore, in this paper, a novel method is provided to decrease the makespan and completion time. Since parallel algorithms use some computing elements to accelerate the search and present a new exploration pattern that is frequently suitable to enhance the quality of the results, in this research, a parallel ant colony optimization algorithm is employed to solve the mentioned problem. The Matlab simulation setting in Net Beans IDE 8.0.2 and Java to simulate the introduced method is applied. According to the obtained results, the suggested procedure has more efficiency than the previous methods. The Matlab simulator outcomes have indicated that the average response time has been improved compared to the PSO-SA and HBC algorithms. Also, the makespan is improved in comparison to GA and MOACSA.

关键词： Flow‐ shop scheduling Parallel ACO algorithm optimization algorithm

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Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching-learning-based optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第8期31卷 p1-24页

作者： Taheri, Seyed Iman Salles, Mauricio B. C. Khan, Irfan Ahmad Univ Sao Paulo Lab Adv Elect Grids LGrid Sao Paulo Brazil Texas A&M Univ Clean & Resilient Energy Syst CARES Lab College Stn TX 77802 USA

The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching-learning-based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi-opposition-based-learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability-value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem.

关键词： distributed energy resources distributed generation distribution network energy management heuristic algorithm optimization algorithm

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Comparison of the Prediction of Effective Moment of Inertia of FRP Rebar-Reinforced Concrete by an optimization algorithm

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MATERIALS 2023年第2期16卷 621-621页

作者： Jang, Nag-Seop Kim, Young-Hwan Oh, Hong-Seob Gyeongsang Natl Univ Dept Civil Engn Jinju 52725 South Korea

FRP (fiber-reinforced polymer)-reinforced concrete members have larger deflection than reinforced concrete members because of the low modulus of elasticity of the FRP bar. In this paper, we proposed a new effective moment of inertia equation to predict the deflection of FRP-reinforced concrete members based on the harmony search algorithm. The harmony search algorithm is used to optimize a function that minimizes the error between the deflection value of the experimental result and the deflection value expected from the specimen's specifications. In the experimental part, four GFRP (Glass Fiber-Reinforced Polymer)-and BFRP (Basalt Fiber-Reinforced Polymer)-reinforced concrete slab specimens were manufactured and tested. FRP-reinforced concrete slabs were reinforced with GFRP and BFRP rebars on spiral rib surfaces. The effects of the FRP reinforcement ratio and balanced reinforcement ratio (rho(f )/rho(fb)), the moment of inertia of the transformed cracked section and the gross moment of inertia (I-cr/I-g), and the cracking moment and the maximum service load moment (M-cr/M-a) on the effective moment of inertia have been considered. The experimental results and predicted results of the flexural testing of concrete slabs reinforced with FRP rebars were compared, and the experimental results were in good agreement with the calculated values using the proposed effective moment of inertia equation.

关键词： FRP rebars effective moment of inertia deflection optimization algorithm harmony search algorithms

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Accident parameter prediction method for lead-bismuth cooled reactor based on a multivariate LSTM network coupled with an optimization algorithm

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ANNALS OF NUCLEAR ENERGY 2023年第1期193卷

作者： Chen, Zhifei Liu, Zijing Ji, Nan Yang, Junkang Wang, Kai Univ Liverpool Brownlow Hill Liverpool L6 1AJ England Univ South China Sch Nucl Sci & Technol Hengyang 421001 Hunan Peoples R China

Accurate acquisition of key parameters of lead-bismuth cooled reactors under accident conditions is a prerequisite for reactor safety analyses. In this work, four optimization algorithms(Particle Swarm algorithm, Genetic algorithm, Quantum genetic algorithm, Whale optimization algorithm) are used to improve the forecast efficiency of a long short-term memory (LSTM) neural network using hyperparameter optimization, and a method is developed to predict the parameters of a lead-bismuth cooled reactor (MARS-3) during a loss of flow accident. A comprehensive evaluation of the proposed method is performed using the TOPSIS technique based on the data samples generated using the sub-channel code SUBCHANFLOW. The results show that the prediction performance of the multivariate LSTM neural network coupled with the particle swarm optimization method is optimal, and its computational efficiency is 438 times that of SUBCHANFLOW. Overall, the study findings can help improve the prediction efficiency for key thermal parameters of lead-bismuth cooled reactors and enhance the emergency response capability of such reactors.

关键词： Multivariate LSTM optimization algorithm Lead-bismuth cooled reactor Accident parameter prediction SUBCHANFLOW

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Underwater Acoustic Sensor Networks: An Energy Efficient and Void Avoidance Routing Based on Grey Wolf optimization algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第4期46卷 3939-3954页

作者： Gola, Kamal Kumar Gupta, Bhumika Teerthanker Mahaveer Univ Fac Engn Dept Comp Sci & Engn Moradabad 244001 India Govind Ballabh Pant Inst Engn & Technol Dept Comp Sci & Engn Pauri Garhwal 246194 Uttarakhand India

The purpose of underwater acoustic sensor networks (UWASNs) is to find varied applications for ocean monitoring and exploration of offshore. In majority of these applications, the network comprises of several sensor nodes deployed at different depths in water. The sensor nodes which are situated in depth, at the sea bed, are unable to communicate unswervingly with those nodes which are close to the surface level;these nodes necessitate multi-hop communication which is facilitated by suitable routing plan. The working of UWASNs is affected by some constraints like high transmission delay, energy consumption, deployment, long propagation delay and high attenuation. Apart from this, the existence of void region in the route can also affect the overall performance of UWASNs. So, the void region can be avoided by considering the best forwarder node. The selection of the best forwarder node depends on depth variance, depth difference, residual energy, and link quality. Apart from this, an angle is also considered to select the best forwarder node. This paper presents an energy efficient and void region avoidance routing. The concept of grey wolf optimization algorithm is used here to select the best forwarder node. The proposed work increases the network lifetime by avoiding the void region and also balancing the network energy. The proposed work is simulated in the MATLAB platform and compared with weighting depth and forwarding area division depth-based routing and energy and depth variance-based opportunistic void avoidance schemes. This work achieves the packet delivery ratio 96% with varying transmission range up to 1000 m at 180 node size. Along with this, it decreases the end-to-end delay and average number of dead nodes up to 53% and 145, respectively. This work also improves the overall network lifetime and reduces the transmission delay. This work also propagates 55% less copies of data packets. Similar to this, some other performance metrics are also explained

关键词： Void avoidance optimization algorithm Delay Energy efficient Network lifetime

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Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion optimization algorithm

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ENERGIES 2023年第20期16卷 7185页

作者： Wu, Yanmin Liu, Jiaqi Wang, Lu An, Yanjun Zhang, Xiaofeng Naval Univ Engn Coll Elect Engn Wuhan 430033 Peoples R China Zhengzhou Univ Light Ind Coll Bldg Environm Engn Zhengzhou 450002 Peoples R China

Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved;the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, mor

关键词： distribution network reconfiguration chaos optimization CPSCSFO algorithm optimization algorithm distributed generation

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Boosted sooty tern optimization algorithm for global optimization and feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Houssein, Essam H. Oliva, Diego Celik, Emre Emam, Marwa M. Ghoniem, Rania M. Minia Univ Fac Comp & Informat Al Minya Egypt Univ Guadalajara CUCEI Dept Innovac Basada Informat & Conocimiento Guadalajara Jalisco Mexico Duzce Univ Dept Elect & Elect Engn Fac Engn Duzce Turkiye Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Mansoura Univ Dept Comp Mansoura Egypt

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classifica-tion accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern optimization algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization.

关键词： Sooty Tern optimization algorithm (STOA) Feature selection Dimensionality reduction Metaheuristics optimization algorithm

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An intensified northern goshawk optimization algorithm for solving optimization problems

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第4期6卷 045267-045267页

作者： Wang, Xiaowei Huangshan Univ Sch Tourism Huangshan Anhui Peoples R China

To enhance the performance of the Northern Goshawk optimization (NGO) algorithm, which has limitations such as inadequate exploration, slow convergence rates, and susceptibility to local optima in later iterations, an Improved Northern Goshawk optimization (INGO) algorithm has been developed. The INGO approach employs a good-point set strategy to obtain a high-quality initial population, generating evenly distributed solutions within the search domain. Incorporating an axial mapping differential variation method facilitates the creation of candidate solutions, broadening the scope for identifying optimal solutions, enhancing the algorithm's ability to avoid local optima, and strengthening its global exploration capabilities. By implementing a Gaussian differential variation strategy, the diversity within the population is increased, which accelerates the convergence rate and improves the algorithm's local search efficiency. For performance evaluation, the INGO algorithm was benchmarked against seven contemporary optimization algorithms using 65 test functions from the CEC2014, CEC2017, and CEC2022 suites. The outcomes indicate that INGO exhibits superior performance regarding convergence velocity, search precision, and stability when compared to the other algorithms.

关键词： northern goshawk optimization differential mutation good point set method optimization algorithm genetic algorithm metaheuristic optimizer

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