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Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

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ENERGY CONVERSION AND MANAGEMENT 2016年 110卷 230-239页

作者： Meo, Santolo Zohoori, Alireza Vahedi, Abolfazl Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy Iran Univ Sci & Technol Dept Elect Engn Ctr Excellence Power Syst Automat & Operat Tehran Iran

In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent magnet flux switching generator for wind application. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network Direct drive wind turbine Outer rotor configuration multi-objective particle swarm optimization Permanent magnet flux switching generator

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New mathematical model for the bi-objective inventory routing problem with a step cost function: A multi-objective particle swarm optimization solution approach

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APPLIED MATHEMATICAL MODELLING 2017年 49卷 302-318页

作者： Dabiri, Nooraddin Tarokh, Mohammad. J. Alinaghian, Mandi Golestan Univ Fac Engn Gorgon Iran KN Toosi Univ Technol Dept Ind Engn Tehran Iran Isfahan Univ Technol Dept Ind & Syst Engn Esfahan *** Iran

Inventory management and satisfactory distribution are among the most important issues considered by distribution companies. One of the key objectives is the simultaneous optimization of the inventory costs and distribution expenses, which can be addressed according to the inventory routing problem (IRP). In this study, we present a new transport cost calculation pattern for the IRP based on some real cases. In this pattern, the transportation cost is calculated as a function of the load carried and the distance traveled by the vehicle based on a step cost function. Furthermore, previous methods usually aggregate the inventory and transportation costs to formulate them as a single objective function, but in non-cooperative real-life cases, the inventory-holding costs are paid by retailers whereas the transportation-related costs are paid by the distributor. In this study, we separate these two cost elements and introduce a bi-objective IRP formulation where the first objective is to minimize the inventory-holding cost and the second is minimizing the transportation cost. We also propose an efficient particle representation and employ a multi-objective particle swarm optimization algorithm to generate the non-dominated solutions for the inventory allocation and vehicle routing decisions. Finally, in order to evaluate the performance of the proposed algorithm, the results obtained were compared with those produced using the augmented epsilon-constraint method, thereby demonstrating the practical utility of the proposed multi-objective model and the proposed solution algorithm. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Augmented epsilon-constraint method Distribution system Inventory routing problem multi-objective particle swarm optimization Step cost function

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Improvement of Time and Frequency Domain Performance of Antipodal Vivaldi Antenna Using multi-objective particle swarm optimization

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2011年第5期59卷 1738-1742页

作者： Chamaani, Somayyeh Mirtaheri, S. Abdullah Abrishamian, Mohammad S. KN Toosi Univ Technol Tehran *** Iran

The design of an antipodal Vivaldi antenna for ultra wideband (UWB) applications is presented. The main purpose of this design is the reduction of three parameters of transient distortion, reflection coefficient and cross polarization level. multi-objective particle swarm optimization (MOPSO) is applied to handle these objectives simultaneously. All antenna simulations are performed using the CST MWS software. Several design cases of the resultant Pareto fronts are compared, and finally, a sample of the Pareto front is fabricated. To assess the time domain performance of the antenna, two identical antennas are placed in an end-to-end orientation. The simulated correlation factor in this scenario is more than 93%. Measurement results show that vertical bar S-11 vertical bar of the developed antenna is less than -10 dB, the cross polarization level is below -11.5 dB and the gain is between 4.5 dBi and 9 dBi over the whole UWB frequency band.

关键词： Antipodal Vivaldi antenna correlation factor multi-objective particle swarm optimization ultrawideband antenna

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A hybrid method of multi-objective particle swarm optimization and k-means clustering and its application to modal parameter estimation in the time-frequency domain

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JOURNAL OF VIBRATION AND CONTROL 2020年第9-10期26卷 769-778页

作者： Yang, Kai Yu, Kaiping Wang, Hui CARDC Mianyang Sichuan Peoples R China HIT Dept Astronaut Sci & Mech Harbin Heilongjiang Peoples R China

Modal parameters provide an insight into the dynamical properties of structures. In the time-frequency domain-based methods, time-frequency ridges contain crucial information on the characteristics of multicomponent signals, and manually extracting time-frequency ridges is a huge burden, especially when long-time time-varying modal parameters are focused on. In this study, time-frequency ridge extraction is converted into a multi-objective optimization problem, and a new hybrid method of multi-objective particle swarm optimization and k-means clustering is proposed to solve such a multi-objective optimization problem. In the hybrid method, the particle swarm is partitioned into sub-swarms by k-means clustering, and the sub-swarms are used to search new solutions for updating a finite-sized external archive, which is used as the exclusive centroids of the k-means clustering. Simultaneously, the finite-sized external archive serves as global best positions of sub-swarms. Both simulated and experimental cases are applied to validate the hybrid method. With the aid of the hybrid method, the influence of varying temperatures on modal parameters of a column beam is experimentally analyzed in detail.

关键词： modal parameter multi-objective particle swarm optimization k-means clustering time-frequency ridge temperature-varying environment

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Automated Test Case Generation for Path Coverage by Using multi-objective particle swarm optimization Algorithm with Reinforcement Learning and Relationship Matrix Strategies

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2024年第8期34卷 1221-1249页

作者： Liang, Shuwen He, Zhitao Beihang Univ Sch Comp Sci & Engn 37 Xueyuan Rd Beijing 100191 Peoples R China

Software testing is an important phase in software life cycle, and test case generation occupies the main part of the testing workload. Path coverage is the most stringent and difficult coverage criteria, so automatic test case generation for path coverage (ATCG-PC) is an important task in software testing. ATCG-PC is usually abstracted as an optimization search problem, and the commonly used solution algorithm is swarm intelligence algorithm. This paper proposes a path coverage test case generation algorithm based on improved multi-objective particle swarm optimization (IMOPSO) algorithm. The algorithm uses the multi-objective strategy to generate test cases for a group of similar paths at a time, and is supplemented by the particle action selection strategy based on reinforcement learning and the secondary search algorithm based on relationship matrix, which makes use of the similarity information between paths to speed up the early convergence and help to quickly reach the optimal solution around the local optimal position. By grouping the paths according to the similarity, the algorithm reduces the test case consumption, and improves the efficiency of test case generation. Experimental results show that, compared with the existing methods, the proposed algorithm can achieve higher path coverage with less test case consumption and is also suitable for large-scale programs.

关键词： Automated test case generation path coverage multi-objective particle swarm optimization reinforcement learning

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Solving a new multi-objective multi-route flexible flow line problem by multi-objective particle swarm optimization and NSGA-II

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JOURNAL OF MANUFACTURING SYSTEMS 2015年 36卷 189-202页

作者： Ghodratnama, A. Jolai, F. Tavakkoli-Moghaddam, R. Kharazmi Univ Dept Ind Engn Fac Engn Karaj Iran Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Univ Tehran Res Inst Energy Management & Planning Coll Engn Tehran Iran

Mass production, meeting the increasing demands of the customers is a necessity. Such a production is mainly dependent on a factory manufacturing called flow line production. This paper deals with special type of production by the name of flexible manufacturing system, assuming the presence of multi processors in each station of a multi-station arrangement. The model debated in the paper possesses three objective functions, the first of which attempts to minimize the weighted delays. The second objective function tries to minimize the capital for the purchase of the processors at stations and the third objective function minimizes the capital dedicated to select the optimum processing route of parts. For the validation of the mathematical model, use has been made of NSAGAII and MOPSO approaches. (C) 2014 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Flexible flow line problem (FFL) Processing route multi-objective particle swarm optimization MOPSO NSGA-II

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Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

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APPLIED SOFT COMPUTING 2011年第1期11卷 1427-1438页

作者： Qasem, Sultan Noman Shamsuddin, Siti Mariyam Univ Teknol Malaysia Fac Comp Sci & Informat Syst Soft Comp Res Grp Skudai 81310 Johor Malaysia

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization(TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Radial basis function network Hybrid learning particle swarm optimization Time variant multi-objective particle swarm optimization multi-objective particle swarm optimization Elitist non-dominated sorting genetic algorithm

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Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter optimization, Machine Learning Techniques, and multi-objective particle swarm optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-22页

作者： Chintakindi, Sanjay Alsamhan, Ali Abidi, Mustufa Haider Kumar, Maduri Praveen King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Adv Mfg Inst Riyadh 11421 Saudi Arabia Univ Leicester Leicester Leics England

The purpose of this paper is to investigate the effect of the annealing process at 1000 degrees C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (psi(1) = 64.5), tool wear (psi(2) = 22.3) and metal removal rate (psi(3) = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows;surface roughness (2.5 mu m, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm(3)/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.

关键词： Monel-400 Annealing Principal component analysis Optuna Machine learning multi-objective particle swarm optimization

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GIS-Based multi-objective particle swarm optimization of charging stations for electric vehicles

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ENERGY 2019年 169卷 844-853页

作者： Zhang, Yue Zhang, Qi Farnoosh, Arash Chen, Siyuan Li, Yan China Univ Petr Acad Chinese Energy Strategy Beijing 102249 Peoples R China IFP Energies Nouvelles IFP Sch 228-232 Ave Napoleon Bonaparte F-92852 Rueil Malmaison France

The rapid development of electric vehicles can greatly alleviate the environmental problems and energy tension. However, the lack of public supporting facilities has become the biggest problem hinders its development. How to reasonably plan the placement of charging stations to meet the needs of electric vehicles has become an urgent situation in China. Different from private charging piles, charging station could help to break the limitation of short range. It also has a special dual attribute of public service and high investment. Therefore, a mathematically optimal model with two objective functions is developed to analyze the relationship between upfront investments and operating costs and service coverage of charging station system and it was solved by particle swarm optimization. Besides, we take into account the conveniences of stations for charging vehicles and their influences on the loads of the power grid. Geographic Information System is used to overlay the traffic system diagram on power system diagram to find the alternative construction sites. In this study, a district in Beijing is analyzed using the proposed method and model. And the following suggestions are given: government should lead the construction of charging station;service ability needs to be enhanced;it is better to make more investment at earlier stage;constructions of charging stations can facilitate EV's development. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Electric vehicle Charging station multi-objective particle swarm optimization GIS

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Optimal shape design of an autonomous underwater vehicle based on multi-objective particle swarm optimization

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NATURAL COMPUTING 2020年第4期19卷 733-742页

作者： Tang, Qirong Li, Yinghao Deng, Zhenqiang Chen, Di Guo, Ruiqin Huang, Hai Tongji Univ Sch Mech Engn Lab Robot & Multibody Syst Shanghai 201804 Peoples R China Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin 150001 Peoples R China

optimization of autonomous underwater vehicle's shape is usually a multi-objective optimization problem, which is essential for autonomousunderwater navigation and manipulation. To overcome the inefficiency of computational fluid dynamics software during the optimization process and thelimitations of traditional single-objective optimization, a novel strategy combining genetic expression programming and crowding distance basedmulti-objective particle swarm algorithm is presented. Its central idea is as follows, several underwater vehicle shapes are analysed to obtain their waterresistances and determine the best underwater robot shape. Shape factor of the bow and shape factor of the stern are employed as design variables, and sample points are selected by the optimal latin hypercube design. Then gene expression programming method is used to establish the surrogate model of resistance and surrounded volume. After that, the surrogate model based on the gene expression programming method is compared with that based on the surface respond method. The results show the superiority of the GEP method. Then the resistance and surrounded volume are set as two optimized variables and Pareto optimal solutions are obtained by using multi-objective particle swarm algorithm. Finally, the optimization results are compared with the hydrodynamic calculations, which shows the method proposed in the paper can greatly reduce the cost of computation and improve the efficiency of optimal shape design for underwater vehicle.

关键词： Autonomous underwater vehicle Shape optimization Gene expression programming multi-objective particle swarm optimization

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