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An Integrated Hybrid algorithm Based on Nature Inspired Evolutionary for Radial Basis Function Neural Network Learning

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2016年第2期25卷 1650004-1650004页

作者： Chen, Zhen-Yao Kuo, R. J. Hu, Tung-Lai DE LIN Inst Technol Dept Business Adm 1Ln 380Qingyun Rd New Taipei 23654 Taiwan Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Keelung Rd Taipei 10607 Taiwan Natl Taipei Univ Technol Dept Business Management 1Sec 3Zhongxiao E Rd Taipei 10608 Taiwan

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.

关键词： Function approximation radial basis function neural network particle swarm optimization algorithm genetic algorithm sales forecasting

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Intellectual Approaches to Improvement of the Classification Decisions Quality on the Base of the SVM Classifier

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Procedia Computer Science 2017年 103卷 222-230页

作者： L. Demidova I. Klyueva Y. Sokolova N. Stepanov N. Tyart Ryazan State Radio Engineering University 59/1 Gagarina Str. Ryazan 390005 Russia Moscow Technological Institute 38A Leninskiy pr. Moscow 119334 Russia

In this paper the hybrid and modified versions of the PSO algorithm applied to improvement of the search characteristics of the classical PSO algorithm in the development problem of the SVM classifier have been offered and investigated. A herewith two hybrid versions of the PSO algorithm assume the use of the classical “Grid Search” (GS) algorithm and the “Design of Experiment” (DOE) algorithm accordingly, and the modified version of the PSO algorithm realizes the simultaneous search of the kernel function type, the parameters values of the kernel function, and also the regularization parameter value. Besides, the questions of applicability of the k nearest neighbors (kNN) algorithm in the development problem of the SVM classifier have been considered.

关键词： support vector machine particle swarm optimization algorithm grid search algorithm hybrid algorithm classification optimization parameters of the kernel function regularization parameter separating hyperplane support vectors.

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A Novel Data-Driven Rolling Force Predictive Model Based on PSO-BAS-HKSVR Approach

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电脑学刊 2021年第4期32卷 25-41页

作者： Ming-Hua Liu Qiang Zhang Ying-Hua Liu Wen-Li Wang

To remedy the defects of the single kernel function and PSO algorithm, a novel rolling force prediction model is proposed, combining particle swarm optimization (PSO) algorithm, beetle antennae search (BAS) algorithm and hybrid kernel function support vector regression (HKSVR), i.e., PSO-BAS-HKSVR model. Hybrid kernel function (HKF) is incorporated to reduce the defect of the single kernel function of support vector regression. In the meantime, PSO algorithm is improved and combined with BAS algorithm to optimize the HKSVR model parameters (C , g ,d, ε ,m) Statistical indicators (R^2, RMSE, MAE and MAPE) are introduced to assess the comprehensive property of the model. The experimental data of the training and testing model originate from the actual production line of the steel plant. Rolling temperature, thickness reduction, initial strip thickness and width, front tension, back tension, roll diameter, and rolling speed are taken as the input variables. Under the identical experimental conditions, compared with the single SVR, PSO-SVR, PSO-HKSVR, BPNN, GRNN and RBF models, PSO-BAS-HKSVR model exhibits the highest prediction accuracy and the optimal generalization ability. As indicated from the results PSO-BAS-HKSVR method is suited for the rolling force prediction and the optimization of model parameters in the hot strip rolling process.

关键词： beetle antennae search algorithm hybrid kernel function particle swarm optimization algorithm rolling force support vector regression

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optimization of inbound vehicle routes in the collection of bio-medical wastes

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Materials Today: Proceedings 2021年 45卷 692-699页

作者： U. Mohamed Faizal R. Jayachitra P. Vijayakumar M. Rajasekar Department of Mechanical Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore 641062 India Department of Mechanical Engineering PSG College of Technology Coimbatore 641062 India

In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and environment. In this article, the inbound logistics involved in the collection of Bio-Medical Waste at a Private Multi-Specialty Hospital located in Coimbatore which contains 59 wards has been improved to avoid time delay. An optimized vehicle routing model has been framed for a set of 6 dedicated vehicles with the objective to minimize the time taken during the collection of BMW. For this purpose a mathematical model is generated and solved using particle swarm optimization algorithm (PSO). The results infer that, by following the optimized vehicle routes, the time delay is totally eliminated and in addition the time taken for collecting the BMW is reduced by 42%, i.e. from 6 h to 3 h 46 min.

关键词： Vehicle routing Bio-medical waste Time delay Inbound logistics particle swarm optimization algorithm

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A Day-Ahead Energy Optimal Configuration of Islanded Microgrid

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电脑学刊 2018年第2期29卷 75-85页

作者： Xiaoxuan Ma Yiping Huang Xiaohui Wang

Microgrid can effectively utilize renewable resources and reduce fossil fuel exploitation and environmental pollution. By setting the microgrid of renewable energy sources as the research object, this paper proposes a day-ahead energy optimization method under island mode. Overall management is realized for electric energy generated by multiple renewable energy sources. Moreover, by combining with energy storage devices and power generation equipment, economic configuration of operation optimization is completed for microgrid system on the basis of particle swarm optimization algorithm under MATLAB environment.

关键词： distributed power economic optimization energy management microgrid particle swarm optimization algorithm

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Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application

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ENERGIES 2023年第9期16卷 3902页

作者： Djouahi, Abdeldjalil Negrou, Belkhir Rouabah, Boubakeur Mahboub, Abdelbasset Samy, Mohamed Mahmoud Univ Kasdi Merbah Ouargla Lab Promot & Valorisat Ressources Sahariennes VPRS BP 511 Ouargla 30000 Algeria Univ Kasdi Merbah Ouargla Elect Engn Dept BP 511 Ouargla 30000 Algeria Beni Suef Univ Fac Engn Elect Engn Dept Bani Suwayf 2722165 Egypt

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

关键词： fuel-cell hybrid electric vehicle particle swarm optimization algorithm hydrogen consumption multi-objective function problem energy management strategy

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Study on the Bike-sharing Inventory Rebalancing and Vehicle Routing for Bike-sharing System

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Transportation Research Procedia 2019年 39卷 624-633页

作者： Lei Shi Yong Zhang Weina Rui Xinzheng Yang School of Transportation Southeast University Southeast university jiulonghu campus Nanjing and 211100 China Transportation Bureau of Huishan District Huishan District Xizhang West Road No.100 Wuxi and 214000 China School of Automobile Chang’an University Middle Section of South 2nd Ring Road Xi’an and 710064 China

Bike-sharing system has been launched in many cities, due to the essential merits. Along with the convenience brought by the rapid development of bike-sharing system, several severe problems also arise. The most serious problem is the uneven distribution of bicycles. Thus the VRP model for bike-sharing inventory rebalancing and vehicle routing is formulated. Additionally, an improved particle swarm optimization (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Five maintenance trucks are designated to execute all delivery tasks required by 25 spots. Capacities of all of the maintenance trucks are almost fully utilized. It is of considerable significance for bike-sharing enterprises to make optimal bike schedule.

关键词： Bike-sharing system inventory rebalancing vehicle routing problem particle swarm optimization algorithm

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Hybrid PSO – SA algorithm for Achieving Partitioning optimization in Various Network Applications

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Procedia Engineering 2012年 38卷 1728-1734页

作者： Shikha Kathpal Rajan Vohra Jaideep Singh R.S. Sawhney Daviet Department of Electronics and Communication Jalandhar India Gndu Department of Electronics Technology Amritsar India Acet Department of Electronics and CommunicationAmritsar India

In this paper we present partitioning for simultaneous cut size and circuit delay minimization. Due to the random search, of simulated annealing algorithms, the solution of a circuit partitioning problem is global optimum. [1] The circuit is divided into partitions and number of interconnections between them is minimized. [2] PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. Experimental result shows that the developed hybrid PSO and SA algorithm can consistently produce the better result than the other algorithms.

关键词： particle swarm optimization algorithm Partitioning Simulated Annealing algorithm Netlist

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An Intelligent Scheduling Strategy of Collaborative Logistics for Mass Customization

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Procedia Engineering 2012年 29卷 2621-2626页

作者： Min-Hui Zhang Xiao-Ling Gao Xin-Yi Zhou Jian Yang School of computer science Sichuan college of Education ChengDu 611130 Chengdu college University of Electronic Science &Technology of China Chengdu 611731

To improve the scheduling optimization of mass customization collaborative logistics, a scheduling solution developed based on the novel particle swarm optimization algorithm was proposed. A mathematic model based on scheduling strategy of mass customization logistics was designed. The novel dynamic particle swarm optimization algorithm framework was given. And simulation experiments were done to validate algorithm. Experiment results show that the proposed algorithm effectively improves the scheduling optimization of mass customization collaborative logistics, which has direct applications for Logistics scheduling

关键词： particle swarm optimization algorithm Mass customization collaborative logistics Logistic scheduling

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Prediction and analysis of bearing vibration signal with a novel gray combination model

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ADVANCES IN MECHANICAL ENGINEERING 2020年第5期12卷

作者： Yuan, Qiang Sun, Yu Zhou, Rui-ping Wen, Xiao-fei Dong, Liang-xiong Wuhan Univ Technol Sch Energy & Power Engn Wuhan Peoples R China Zhejiang Ocean Univ Sch Port & Transportat Engn Zhoushan 316000 Zhejiang Peoples R China

Bearings are the core components of ship propulsion shafting, and effective prediction of their working condition is crucial for reliable operation of the shaft system. Shafting vibration signals can accurately represent the running condition of bearings. Therefore, in this article, we propose a new model that can reliably predict the vibration signal of bearings. The proposed method is a combination of a fuzzy-modified Markov model with gray error based on particle swarm optimization (PGFM (1,1)). First, particle swarm optimization was used to optimize and analyze the three related parameters in the gray model (GM (1,1)) that affect the data fitting accuracy, to improve the data fitting ability of GM (1,1) and form a GM (1,1) based on particle swarm optimization, which is called PGM (1,1). Second, considering that the influence of historical relative errors generated by data fitting on subsequent data prediction cannot be expressed quantitatively, the fuzzy mathematical theory was introduced to make fuzzy corrections to the historical errors. Finally, a Markov model is combined to predict the next development state of bearing vibration signals and form the PGFM (1,1). In this study, the traditional predictions of GM (1,1), PGM (1,1), and newly proposed PGFM (1,1) are carried out on the same set of bearing vibration data, to make up for the defects of the original model layer by layer and form a set of perfect forecast system models. The results show that the predictions of PGM (1,1) and PGFM (1,1) are more accurate and reliable than the original GM (1,1). Hence, they can be helpful in the design of practical engineering equipment.

关键词： particle swarm optimization algorithm gray system theory fuzzy mathematical theory Markov data forecasting shafting vibration

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