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Application of multi-SVM classifier and hybrid GSAPSO algorithm for fault diagnosis of electrical machine drive system

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ISA TRANSACTIONS 2023年 133卷 529-538页

作者： Ding, Shichuan Hao, Menglu Cui, Zhiwei Wang, Yinjiang Hang, Jun Li, Xueyi Anhui Univ Sch Elect Engn & Automat Anhui Peoples R China

A method, being based on multi-class support vector machine (SVM) classifier and hybrid particle swarm optimization (PSO) and gravity search algorithm (GSA), is presented to diagnose the faults in electrical motor drive system. In this method, the global search ability of PSO and the local search ability of GSA are integrated to combine the advantages of PSO and GSA, and the multi-class SVM classifier is optimized by the hybrid GSAPSO algorithm to improve classification performance. To test the presented method, a series of simulation and experiment are studied. The diagnostic results display that the presented method can gain more precise classification accuracy than multi-class SVM with PSO and multi-class SVM with GSA.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Electrical machine Fault diagnosis Support vector machine gravity search algorithm Particle swarm optimization

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Single and multiple humanoid path planning using Hill valley approach applied to gravitational drift in Gravitational search algorithm

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ROBOTICA 2023年第12期41卷 3627-3648页

作者： Vikas Parhi, Dayal R. Natl Inst Technol Mech Engn Dept Robot Lab Rourkela Odisha India

In this paper, the Hill Valley (HV) approach is applied to the drifting masses or agents in the basic Gravitational search algorithm (GSA) for the path planning of humanoid robots. The drift in lighter masses toward the heavier mass creates a localized area, where the probability of obtaining a globally optimal solution is very high. So, the present work is focused on exploiting the area to tackle local optima and provide the best steering angle for the humanoids to navigate. The HV approach is applied to the basic GSA model, at the later stages, to improve the overall computational time and cost. The robustness of the proposed controller was tested in both simulation and experimental environments and compared with the previous research. The results obtained from the proposed controller showed a significant improvement in the overall path length and time taken. Path smoothness was also given equal importance during path planning to ensure stability. The multi-robot navigational scheme was performed using the Dining Philosopher's model to avoid dynamic collision among the humanoids. The percentage deviation in the results was within the acceptable limits. To further check the effectiveness of the proposed technique, the proposed approach was compared with the vision-based navigation in danger space.

关键词： Humanoid navigation Hill Valley approach gravity search algorithm Hybrid techniques Dining Philosopher's model

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Memetic frog leaping algorithm for global optimization

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SOFT COMPUTING 2019年第21期23卷 11077-11105页

作者： Tang, Deyu Liu, Zhen Yang, Jin Zhao, Jie Guangdong Pharmaceut Univ Sch Med Informat & Engn Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Technol Sch Management Dept Informat Management Engn Guangzhou 510520 Guangdong Peoples R China Amer Univ Dept Comp Sci Washington DC 20016 USA

Developing an effective memetic algorithm that integrates leaning units and achieves the synergistic coordination between exploration and exploitation is a difficult task. In this paper, we propose a memetic algorithm based on the shuffled frog leaping algorithm, which is fulfilled by three units: memetic diffusion component, memetic evolutionary component and memetic learning component. Memetic diffusion component enhances the diversity of population by the shuffled process. Memetic evolutionary component accomplishes the exploitation task by integrating the frog leaping rule, geometric center, Newton's gravitational force-based gravitational center and Levy flight operator. Memetic learning component improves the exploration by an adaptive learning rule based on the individual selection and the dimension selection. In order to evaluate the effectiveness of the proposed algorithm, 30 benchmark functions and a real-world optimization problem are used to compare our algorithm against 13 well-known heuristic methods. The experimental results demonstrate that the performance of our algorithm is better than others for the continuous optimization problems.

关键词： Memetic algorithm Shuffled frog leaping algorithm gravity search algorithm Levy flight Continuous optimization

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Predicting discharge coefficient of weir-orifice in closed conduit using a neuro-fuzzy model improved by multi-phase PSOGSA

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APPLIED WATER SCIENCE 2024年第3期14卷 40页

作者： Adnan, Rana Muhammad Khosravinia, Payam Kisi, Ozgur Nikpour, Mohammad Reza Dai, Hong-Liang Osmani, Mazyar Ghazaei, Seyyede Aniseh Guangzhou Univ Coll Architecture & Urban Planning Guangzhou 510006 Peoples R China Univ Kurdistan Fac Agr Dept Water Sci & Engn Sanandaj Iran Luebeck Univ Appl Sci Dept Civil Engn D-23562 Lubeck Germany Ilia State Univ Civil Engn Dept Tbilisi 0162 Georgia Univ Mohaghegh Ardabili Fac Agr & Nat Resources Dept Water Engn Ardebil Iran Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China KN Toosi Univ Technol Fac Civil Engn Tehran Iran

This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir-orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h/D (the ratio of upstream water depth to channel diameter), W/D (the ratio of orifice opening height to channel diameter), H/D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H/D parameter deteriorated the models' performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.

关键词： Combined weir-orifices Dimensionless experimental discharge Modeling Particle swarm optimization gravity search algorithm Neuro-fuzzy system

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A new insight into metaheuristic optimization method using a hybrid of PSO, GSA, and GWO

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STRUCTURES 2022年 44卷 1168-1189页

作者： Biabani, Fatemeh Shojaee, Saeed Hamzehei-Javaran, Saleh Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

This article considers the Particle Swarm Optimization (PSO), gravity search algorithm (GSA), and Gray Wolf Optimizer (GWO) robust varieties to obtain fresh insight into solving different types of engineering problems without any adherence to the parameters. In the proposed algorithm, the hybridizing of gravity search algo-rithm, Particle swarm optimization, and Gray wolf optimizer (HGPG) utilize to introduce appropriate numerical optimization, and the theoretical findings are supported by solving several benchmark problems. Achieving good results in mathematical functions (CEC2005, 2021) compared with the GWO, GSA, and PSO methods and some other well-known heuristic algorithms shows an enhanced performance of the introduced method compared to the others. The method in a specific case presents a solution to minimize the weight of space truss structures to a logical level and economize the construction of these structures. The results show that the new algorithm made a notable improvement in both exploration and exploitation. Furthermore, a probabilistic metric analysis has also been made of how much better the proposed algorithm is.

关键词： Hybrid metaheuristic algorithms gravity search algorithm Gray wolf optimizer Particle swarm optimization Structural optimization problems

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Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm

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ENERGY CONVERSION AND MANAGEMENT 2011年第1期52卷 374-381页

作者： Li, Chaoshun Zhou, Jianzhong Huazhong Univ Sci & Technol Coll Hydroelect Digitizat Engn Wuhan 430074 Peoples R China

Parameter identification of hydraulic turbine governing system (HTGS) is crucial in precise modeling of hydropower plant and provides support for the analysis of stability of power system. In this paper, a newly developed optimization algorithm, called gravitational search algorithm (GSA), is introduced and applied in parameter identification of HTGS, and the GSA is improved by combination of the search strategy of particle swarm optimization. Furthermore, a new weighted objective function is proposed in the identification frame. The improved gravitational search algorithm (IGSA), together with genetic algorithm, particle swarm optimization and GSA, is employed in parameter identification experiments and the procedure is validated by comparing experimental and simulated results. Consequently, IGSA is shown to locate more precise parameter values than the compared methods with higher efficiency. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Parameter identification Hydroturbine governing system Modeling gravity search algorithm Genetic algorithm Particle swarm optimization

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Hexapod: A Spider for Terrain and Obstacle Shunning by Arduino

Hexapod: A Spider for Terrain and Obstacle Shunning by Ardui...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Jagtap, Vitthal B. Jagdale, Amol B. Kadu, Pankaj S. SKN Sinhgad COE Pandharpur Dept Elect Pandharpur MS India

ISBN: (纸本)9781467399395

Robots are controlling today's tech-machines scenario and world is ready to accept the new trends the for useful operations. In some industries, a part of work which signify threat and consequently, very strict security rules e.g. some of the work in nuclear plant, where human can't go or its should be avoided as well as such condition such as underwater exploration even on terrain surface too where human can't go on defined destination or somehow it's difficult to reach on defined destination even in surface such as rock, mud, natural water flow, sometimes it's impossible to move for any human Here we come up with the solution for this type of problem to develop a Spider robot i.e. hexapod, so that it will overcome not only above examples but it will be a spider robot base system which will be used anywhere in the similar type of situations. Gravitational search algorithm would be utilized for gravity search and to run the same system like insects. We would explore the parameters from gravity algorithm for stabilization of body of the Spider system We will implement artificial intelligence part into the same system as well as GSA algorithm so that spider's locomotion will be like insects and dew to AI, avoidance of obstacle is possible. The physical spider robot system controlled by arduino technology and motors with fabricated body. For development of spider robot we are in planning to use Insects as source of inspiration because the characteristics from the insects such as gaits based on traveling high speed, avoiding obstacles very fast, ability to easily navigate on uneven terrain surface.

关键词： Spider robot gravity algorithm gravity search algorithm biological robot

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Adaptive Weighted Hybrid PSOGSA algorithm and its Application in Power System Economic Load Dispatch Problem 32

Adaptive Weighted Hybrid PSOGSA Algorithm and its Applicatio...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Pu, Xingcheng Zhao, Longlong Xiong, Chaowen Chongqing Univ Posts & Telecommun Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China

ISBN: (纸本)9781728158556

gravity search algorithm (GSA) is a swarm intelligence optimization algorithm based on the gravity law. The standard GSA algorithm has strong global search capability, while its convergence speed is slow. The Particle Swarm Optimization (PSO) algorithm has high convergence speed and search efficiency. Based on the advantages of the above two algorithms, a hybrid algorithm(PSOGSA) is proposed in this paper, and two adaptive weighted update strategies are introduced into the optimization process to improve the search accuracy of the hybrid algorithm. At the same time, we added variable mutation probability to solve the problem that particles are easily be trapped in local optimum. In order to verify the effectiveness of the two improved hybrid algorithms, the two algorithms are applied to the power system economic load dispatch (ELD) problem. Power generation cost optimization performance tests are computed for three groups of power systems with different unit numbers. The simulation results show that the two adaptive weighted hybrid algorithms which are proposed in this paper can effectively reduce the generation cost of the power system.

关键词： Adaptive Weighted Power System Economic Load Dispatch Problem Particle Swarm Optimization algorithm gravity search algorithm

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Hybrid Gs-Ga algorithm for Parallel Machine Scheduling Problem with Different Transport Vehicles

Hybrid Gs-Ga Algorithm for Parallel Machine Scheduling Probl...

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作者： Hongxiang ZHANG School of Management Hefei University of Technology

This paper studies the scheduling problem of the same kind of parallel machines with different transportation vehicle *** scheduling problem model is composed of a single warehouse and multiple manufacturers located in different geographical *** manufacturer has the same batch processor,and several vehicles assemble the jobs in the warehouse in batches,transport them to different manufacturers and return *** manufacturer of vehicle transportation depends on the destination of the jobs when the code is *** the manufacturer's place,because the capacity of the vehicle may exceed the maximum capacity of the batch processor,the job will be re batch for processing after it *** modeling,a 2n dimensional code is designed,and the gravitational search algorithm is *** mutation and crossover process of genetic algorithm are added.A hybrid genetic algorithm based on gravitational search is *** algorithm is used to optimize the sum of manufacturing time span and transportation *** effectiveness of the algorithm is verified by different scale simulation experiments.

关键词： Batch processing Transportation coordination scheduling gravity search algorithm transportation cost

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Adaptive Weighted Hybrid PSOGSA algorithm and its Application in Power System Economic Load Dispatch Problem

Adaptive Weighted Hybrid PSOGSA Algorithm and its Applicatio...

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第32届中国控制与决策会议

作者： Xingcheng Pu Longlong Zhao Chaowen Xiong Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing University of Posts and Telecommunications School of Automation Chongqing University of Posts and Telecommunications

gravity search algorithm（GSA） is a swarm intelligence optimization algorithm based on the gravity *** standard GSA algorithm has strong global search capability,while its convergence speed is *** Particle Swarm Optimization（PSO） algorithm has high convergence speed and search *** on the advantages of the above two algorithms,a hybrid algorithm（PSOGSA） is proposed in this paper,and two adaptive weighted update strategies are introduced into the optimization process to improve the search accuracy of the hybrid *** the same time,we added variable mutation probability to solve the problem that particles are easily be trapped in local *** order to verify the effectiveness of the two improved hybrid algorithms,the two algorithms are applied to the power system economic load dispatch（ELD） *** generation cost optimization performance tests are computed for three groups of power systems with different unit *** simulation results show that the two adaptive weighted hybrid algorithms which are proposed in this paper can effectively reduce the generation cost of the power system.

关键词： Adaptive Weighted Power System Economic Load Dispatch Problem Particle Swarm Optimization algorithm gravity search algorithm

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