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Prediction of the temperature in a Chinese solar greenhouse based on LSSVM optimized by improved PSO

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2016年 122卷 94-102页

作者： Yu, Huihui Chen, Yingyi Hassan, Shahbaz Gul Li, Daoliang China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Agr Informat Acquisit Technol Beijing 100083 Peoples R China Beijing Engn & Technol Res Ctr Internet Things Ag Beijing 100083 Peoples R China

Predictions of the Chinese solar greenhouse temperatures are important because they play a vital role in greenhouse cultivation, with solar greenhouse crops susceptible to potential losses because of cold and hot temperatures. Therefore, it is important to set up a precise predictive model of temperature that can predict the occurrence of temperatures several hours before head to reduce financial losses. This paper presents a novel temperature prediction model based on a least squares support vector machine (LSSVM) model with parameters optimized by improved particle swarm optimization (IPSO). The IPSO with probability of mutation was employed to optimize the required hyper parameters of the LSSVM model. The performance of the IPSO-LSSVM model was compared with traditional modeling approaches by applying it to, predict solar greenhouse temperatures, and the results showed that its predictions of the maximum and minimum temperature were more accurate than those of the standard support vector machine (SVM) and Back propagation neural network (BPNN). Therefore, it is a suitable and effective method for predicting the Chinese solar greenhouse temperatures. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Greenhouse Temperature Predictions Least squares support vector machine particle swarm optimization algorithm

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A novel classification method: A hybrid approach based on extension of the UTADIS with polynomial and PSO-GA algorithm

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APPLIED SOFT COMPUTING 2016年 49卷 56-70页

作者： Esmaelian, Majid Shahmoradi, Hadi Vali, Masoumeh Univ Isfahan Dept Management Hezarjerib StAzadi Sq Esfahan Iran Persian Gulf Univ Dept Management Bushehr 75168 Iran

In this study, a new multi-criteria classification technique for nominal and ordinal groups is developed by expanding the UTilites Additives DIScriminantes (UTADIS) method with a polynomial of degree T which is used as the utility function rather than using a piecewise linear function as an approximation of the utility function of each attribute. We called this method as PUTADIS. The objective is calculating the coefficients of the polynomial and the threshold limit of classes and weight of attributes such that it minimizes the number of misclassification error. Estimation of unknown parameters of the problem is calculated by using a hybrid algorithm which is a combination of particle swarm optimization algorithm (PSO) and Genetic algorithm (GA). The results obtained by implementing the model on different datasets and comparing its performance with other previous methods show the high efficiency of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Classification Utility function UTADIS method Genetic algorithm particle swarm optimization algorithm

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On the Localization algorithm of Wireless Sensor Network and Its Application

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2017年第3期13卷 40-51页

作者： Jia, Honglei Zheng, Jiaxin Wang, Gang Chen, Yulong Huang, Dongyan Yuan, Hongfang Jilin Univ Coll Biol & Agr Engn Changchun 130022 Peoples R China Jilin Univ Minist Educ Key Lab Bion Engn Changchun 130022 Peoples R China

This paper carries out in-depth and meticulous analysis of the DV-Hop localization algorithm for wireless sensor network. It improves the DV-Hop algorithm into a node localization algorithm based on one-hop range, and proposes the centroid particle swarm optimization localization algorithm based on RSSI by adding the RSSI and particle swarm optimization algorithm to the traditional centroid localization algorithm. Simulation experiment proves that the two algorithms have excellent effect.

关键词： DV-Hoplocalization algorithm WSN localization one-hop RSSI particle swarm optimization algorithm

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The Berth-Quay Cranes and Trucks Scheduling optimization Problem by Hybrid Intelligence Swam algorithm

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2017年第2期11卷 74-89页

作者： Liu, Yi Shahbazzade, Sabina Hangzhou Dianzi Univ Sch Management Hangzhou Zhejiang Peoples R China Univ Calif Berkeley Elect Engn & Comp Sci Berkeley CA 94720 USA

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. The proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.

关键词： Artificial Fish swarm algorithm Bioinformatics Computational Intelligence Container Truck Scheduling Problem particle swarm optimization algorithm Quay Crane Scheduling Problem

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A simple and quick control strategy for a class of first-order nonholonomic manipulator

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NONLINEAR DYNAMICS 2016年第4期85卷 2261-2276页

作者： Lai, Xu-Zhi Wang, Ya-Wu Cao, Jun-Qing Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

This paper presents a simple and quick control strategy for a class of first-order nonholonomic manipulator: planar n-link manipulator with a passive first joint (no gravity). The control target is driving its endpoint from any initial position to any target position. First, we reduce the planar n-link manipulator to a planar three-link one by maintaining the states (angles and angular velocities) of n-3 active links in the initial value all the time. That is, we only adjust the states of the remaining two active links in the whole control process, and these two adjusted active links are chosen to guarantee that the target position is in the reachable region of the planar n-link manipulator by using the enumeration method. Then, we divide the whole control process into two stages for the reduced planar three-link manipulator. In each stage, the manipulator becomes a planar two-link one by maintaining the states of one adjusted active link to be the constant value. The state constraint existing between the passive first link and the adjusted active link is obtained by using the integral characteristics of a planar two-link manipulator. Meantime, the geometric constraint between the position of the endpoint and angles of all joints is obtained based on the homogeneous coordinate transformation method. According to the above two kinds of constraint, the target angles of the two adjusted active links are calculated by employing the particle swarm optimization algorithm. When the two adjusted active links are controlled to their target angles in turn, the control target of the planar n-link manipulator is completed. Finally, simulation results demonstrate that the proposed control strategy is valid and rapid.

关键词： Underactuated manipulator Nonholonomic constraint Enumeration method particle swarm optimization algorithm Lyapunov function

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Multi-label classification algorithm research based on swarm intelligence

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2016年第4期19卷 2075-2085页

作者： Wu, Qinghua Liu, Hanmin Yan, Xuesong Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Hubei Peoples R China Wuhan Inst Ship Bldg Technol Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

Since data and resources have massive feature and feature of data are increasingly complex, traditional data structures are not suitable for current data anymore. Therefore, traditional single-label learning method cannot meet the requirements of technology development and the importance of multi-label leaning method becomes more and more highlighted. K-Nearest Neighbor (KNN) classification method is a lazy learning method in data classification methods. It does not need data training process and theoretical system is mature. In addition, principle and implementation is simple. This paper proposed improvements strategies only considers numerical feature of sample KNN when classifying, but not consider the disadvantage of sample structure feature. This paper introduced particle swarm optimization algorithm into KNN classification and make adjustments to Euclidean distance formula in traditional KNN classification algorithm and add weight value to each feature. Using adjusted distance formula to train training data through particle swarm optimization algorithm and optimized a set of weight value for all features and put these optimized weight values to adjusted distance formula and calculated the distance between each example in test data set and in training data set and predict the test data set. Experiment results show that weighted KNN classification algorithm based on particle swarm optimization algorithm can achieve better classification accuracy than traditional KNN classification algorithm.

关键词： Multi-label K-Nearest Neighbor particle swarm optimization algorithm Weighted KNN classification algorithm

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考虑车辆限速和风险公平性的危险品运输网络双目标优化模型

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管理科学 2019年第5期

作者：王伟张宏刚丁黎黎张文思

在危险品运输网络中，对不同路段上危险品运输车辆的最高车速设置不同的限制值，能在很大程度上影响危险品运输商的路径选择，从而导致不同的危险品运输网络总风险和总成本。本文首先基于车辆限速的方法，构建了考虑风险公平性的危险品... 详细信息

在危险品运输网络中，对不同路段上危险品运输车辆的最高车速设置不同的限制值，能在很大程度上影响危险品运输商的路径选择，从而导致不同的危险品运输网络总风险和总成本。本文首先基于车辆限速的方法，构建了考虑风险公平性的危险品运输网络优化的双层规划模型，上层规划以网络总风险和总时间成本的最小化为目标，并通过增加路段容量和区域风险约束以确保风险分布的公平性，下层规划以危险品运输企业的总时间成本最小化为目标；然后，采用粒子群优化算法求解了该模型；最后，通过两个算例验证了模型和算法的有效性。计算结果表明，政府部门运用车辆限速的方法，并且考虑风险平等性约束，在兼顾运输企业效益的同时，能有效地降低危险品运输网络的总风险和保证区域风险分布的公平性。

关键词：公路运输车辆限速粒子群优化算法危险品运输风险公平性双层规划　　Highway Transportation Vehicle Speed Limit particle swarm optimization algorithm Risk Equity Bilevel Programming

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Application of improved support vector regression model for prediction of deformation modulus of a rock mass

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ENGINEERING WITH COMPUTERS 2016年第4期32卷 567-580页

作者： Fattahi, Hadi Arak Univ Technol Dept Min Engn Arak Iran

Deformation modulus of a rock mass is one of the crucial parameters used in the design of surface and underground rock engineering structures. Determination of this parameter by testing cylindrical core samples is almost impossible due to the presence of discontinuities. Due to the problems in determining the deformability of jointed rock masses at the laboratory-scale, various in situ test methods such as plate loading tests, dilatometer etc. have been developed. Although these methods are currently the best techniques, they are expensive and time-consuming, and present operational problems. To overcome this difficulty, in this paper, presents the results of the application of hybrid support vector regression (SVR) with harmony search algorithm , differential evolution algorithm and particle swarm optimization algorithm (PSO). The optimized models were applied to available data given in open source literature and the performance of optimization algorithm was assessed by virtue of statistical criteria. In these models, rock mass rating (RMR), depth, uniaxial compressive strength of intact rock (UCS) and elastic modulus of intact rock (E (i)) were utilized as the input parameters, while the deformation modulus of a rock mass was the output parameter. The comparative results revealed that hybrid of PSO and SVR yield robust model which outperform other models in term of higher squared correlation coefficient (R (2)) and variance account for (VAF) and lower mean square error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE).

关键词： Deformation modulus Support vector regression Harmony search algorithm Differential evolution algorithm particle swarm optimization algorithm

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Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups

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RENEWABLE ENERGY 2016年 88卷 247-261页

作者： Abdollahzadeh, Hadi Atashgar, Karim Abbasi, Morteza Malek Ashtar Univ Technol Dept Ind Engn Tehran 158751774 Iran

Several different models provided by researchers to maintain a wind turbine, but most of these models only focused on the case involved a single objective optimization problem. In practice, real cases of wind farms lead to multi-objective approach to optimize maintenance efforts. In this paper, based on an opportunistic approach, a multi-objective based model is proposed to optimize the maintenance of a farm involved several different types of wind turbines. The assumptions of stochastic behavior of wind velocity as well as the existence of a limited number for maintenance groups are also considered in this new approach. The proposed model considering imperfect maintenance, attempts (1) maximizing the expected rate of energy and (2) minimizing the total expected costs related to maintenance efforts. The opportunistic approach is also provided by the component's reliability threshold values. The comparative analysis addresses that the capability of the proposed model is more efficient compared to models addressed in literature. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Opportunistic maintenance Wind farm Three-phase simulation Imperfect maintenance Multi-objective particle swarm optimization algorithm

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A new data classification method based on chaotic particle swarm optimization and least square-support vector machine

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 147卷 147-156页

作者： Liu, Fang Zhou, Zhiguang Zhejiang Univ Finance & Econ Inst Informat Hangzhou 310018 Zhejiang Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China

In order to improve the classification accuracy in chemometrics data, chaotic optimization algorithm (COA) and particle swarm optimization (PSO) algorithm are introduced into least square-support vector machine (LS-SVM) model in order to propose an optimized LS-SVM model and a novel data classification (CPL-SVM) method in this paper. In the proposed CPL-SVM method, the COA with the randomness and ergodicity is used to chaotically process the initial position and local extreme position of parade in the PSO algorithm in order to obtain a chaotic particle swarm optimization (CPSO) algorithm, and the CPSO is used to select and optimize the important parameters of LS-SVM, then the optimized parameters are used to obtain a better CPL-SVM classification method. The choice randomness of parameters is avoided and the selection workload of parameters is reduced. And this method can not only overcome the time-consuming and blindness of cross validation method, but also reflect small sample learning ability. In order to verify the effectiveness of CPL-SVM method, binary classification data, IRIS flower data and three relevant data sets with pharmacodynamic properties of drug are selected in this paper. The experiment results show that the proposed CPL-SVM method takes on the better learning performance, strong generalization ability, best sensitivity, Matthews correlation coefficient and classification accuracy. And it can effectively avoid the isolated effects of sample in the learning process. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Chaotic optimization algorithm particle swarm optimization algorithm Least square-support vector machine (LS-SVM) Data classification Generalization ability Classification accuracy

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