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A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4198-4205页

作者： Deng, Wu Li, Wen Yang, Xin-hua Dalian Jiaotong Univ Software Inst Dalian 116028 Peoples R China Dalian Univ Technol Minist Educ Key Lab Adv Design & Intelligent Comp Dalian Peoples R China Xiangtan Univ Key Lab Intelligent Mfg Hunan Prov Xiangtan Peoples R China

A novel hybrid optimization algorithm combining computational intelligence techniques is presented to solve the multifactor highway passenger volume prediction problem. In this paper, we can get and discretize a reduced decision table, which implies that the number of evaluation criteria such as travel quantity, fixed-asset investment, railway mileage, and waterway passenger volume are reduced with no information loss through rough set theory (RST) method. Particle swarm optimization (PSO) algorithm based on the random global optimization is inducted into the network training. The PSO algorithm is used for glancing study in order to confirm the initial values, and then the back propagation neural network (BPNN) is used for given accuracy to found the PSO-BPNN model. And this reduced information is used to form a classification rule set, which is regarded as an appropriate input parameter to training PSO-BPNN model. The RST-PSO-BPNN model is obtained to forecast highway passenger volume. The rules developed by RST analysis show the best prediction accuracy if a case matches any one of the rules. The keystone of this hybrid optimization algorithm is using rules developed by RST for an object that matches any one of the rules and the PSO-BPNN model for one that does not match any of them. The effectiveness of our optimization algorithm was verified by experiments comparing the traditional gray model method. For the experiment, highway passenger volumes of China during the period 1995-2009 were selected, and for the validation, the novel hybrid optimization algorithm is reliable. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Highway passenger volume prediction Back propagation neural network Rough set Computational intelligence hybrid optimization algorithm Particle swarm optimization algorithm Discretization

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Economical planning of fuel cell vehicle-to-grid integrated green buildings with a new hybrid optimization algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第13期47卷 8514-8531页

作者： Li, Hao Sun, Bo Hao, Jianjun Zhao, Jianguo Li, Jianchang Khakichi, Aroos Hebei Agr Univ Coll Mech & Elect Engn Baoding 071001 Hebei Peoples R China Arian Co Elect Engn Dept Yerevan Armenia

Popularity of fuel cell electric vehicles (FCVs) is an important criterion for solving the global problem of reducing CO2 emissions. However, the overall cost of FCVs and hydrogen fuel production is relatively high, so FCV promotion is slow. Considering that FCVs have near-zero CO2 emissions and high endurance, which is suitable for vehicle-to-grid (V2G) systems, this study aims to analyze the economic potential of the fuel cell vehicle-to-grid (FCV2G) systems to promote FCVs to the highest level. For this purpose, a large-scale green building was first selected as the research target and an agent to provide V2G services for the power grid. Then, Monte Carlo method was used to simulate the vehicle visiting time. A discharge model was also developed. Considering CO2 emission price and self-elasticity coefficient of discharge price, an overall economic optimization model was presented. Then, the hybrid algorithm of competitive swarm optimization (CSO) and imperialist competitive algorithm (ICA) was applied to optimize the model, which not only led to definite results and reduced standard deviation, but also eliminated the weakness of the CSO, i.e., convergence speed and poor performance in some benchmark functions. The simulation results indicated the proposed algorithm had faster convergence and more accuracy in finding the optimal solution than other optimization algorithms. Moreover, the overall economic profit improved in the presence of FCVs. Finally, sensitivity analysis was performed on six parameters, including daily electricity price, battery cost, fuel cell cost, CO2 emission price, power grid carbon emission, and hydrogen cost. The results showed FCV2G system had high development potential as well as great economic profit increasing over time. (C) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Economic potential analysis Fuel cell hybrid optimization algorithm Electric vehicles (EVs)

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A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 85卷 254-268页

作者： Zhang, Zichen Ding, Shifei Jia, Weikuan China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Peoples Republ China Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 265000 Shandong Peoples R China

Based on Cuckoo Search (CS) and Differential Evolution (DE), a novel hybrid optimization algorithm, called CSDE, is proposed in this paper to solve constrained engineering problems. CS has strong ability on global search and less control parameters, but easy to suffer from premature convergence and lower the density of population. DE specializes in local search and good robustness, however, its convergence rate is too late to find the satisfied solution. Furthermore, these two algorithms are both proved to be especially suitable for engineering problems. This work divides population into two subgroups and adopts CS and DE for these two subgroups independently. By division, these two subgroups can exchange useful information and these two algorithms can utilize each other's advantages to complement their shortcoming, thus avoid premature convergence, balance the quality of solution and the computation consumption, and find satisfactory global optima. Due to the tremendous design variables and constrained conditions of engineering problems, single optimizer failed to meet the requirement of precision, so hybrid optimization algorithms (such like CSDE) is the most promising mean to complete this job. Simulation results reveal that CSDE has more ability to find promising results than other 12 algorithms (including traditional algorithms and state-of-the-art algorithm) on 30 unconstrained benchmark functions, 10 constrained benchmark functions and 6 constrained engineering problems.

关键词： hybrid optimization algorithm Cuckoo search Differential evolution Constrained engineering problems

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A P systems based hybrid optimization algorithm for parameter estimation of FCCU reactor-regenerator model

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CHEMICAL ENGINEERING JOURNAL 2012年 211卷 508-518页

作者： Yang, Shipin Wang, Ning Zhejiang Univ Natl Key Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Zhejiang Peoples R China

Fluid catalytic cracking unit (FCCU) reactor-regenerator is a complex multivariable system and suitable model is useful to understand and design FCCU reaction-regeneration processes. Inspired by the concepts of P systems (membrane computing) and quantum computing, a P systems based hybrid optimization algorithm (PHOA) is proposed to estimate the parameters of FCCU reactor-regenerator model. The expansion of the search space makes the novel algorithm realize exploration and the dynamic contraction of the search space makes it realize exploitation. The quantum update rules and adaptive mutation rule in combination with the new membrane structure contribute to the improvement of global searching performance. Studies on some benchmark functions indicate that the PHOA outperforms DNA based genetic algorithm (DNA-GA) and genetic algorithm (GA) both in search efficiency and accuracy. With the parameters obtained by PHOA, the transfer function matrix model of FCCU reactor-regenerator in an oil refinery is established. Experimental results reveal that model curves gained by this proposed method are in good agreement with the measured data with smaller sum of squared error. The effectiveness of the model is also validated by experiments. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Parameter estimation P systems hybrid optimization algorithm Reactor-regenerator kinetic model Fluid catalytic cracking unit (FCCU)

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Automatic ECG analysis system with hybrid optimization algorithm based feature selection and classifier

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第1期43卷 627-642页

作者： Kaliappan, Manikandan Govindan, Sumithra Manimegalai Kuppusamy, Mohana Sundaram Sona Coll Technol Dept Biomed Engn Salem Tamil Nadu India Dr NGP Inst Technol Dept ECE Coimbatore Tamil Nadu India KPR Inst Engn & Technol Dept EEE Coimbatore Tamil Nadu India

Cardio vascular disease threatens human life with higher mortality rate. Therefore it is quite important to monitor. An arrhythmia is an abnormal heart beat and rhythm which causes the disease. The best tool to find the heart rhythm of heart is Electro Cardiogram (ECG) which provides information about the different types of arrhythmias. This paper aims at proposing an automatic framework by employing multi-domain features to classify ECG signals. Proposed work uses optimum method of feature selection to improvise the efficiency of the classification process. A hybrid optimization algorithm is used for feature selection and proposed to optimize the parameters of the existing Support Vector Machine (SVM) classifier. Proposed hybrid optimization algorithm was developed using Particle Swarm optimization (PSO) and Migration Modified Biogeography Based optimization (MMBBO) algorithm. algorithm provides an improved solution to the optimizing the parameters of ECG signals. Results are evaluated by implementing in MATLAB software and the performance is justified with comparative analysis. The proposed framework enhances the process of automatic prediction of various arrhythmias or rhythm abnormalities which performs in gaining better accuracy. For data sets, the average classification accuracy of this method is 97.89%. This result is an improvement of 4-5% over the comparison of other methods.

关键词： Heart disease arrhythmia feature selection hybrid optimization algorithm classification particle swarm optimization

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A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type

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ENERGY 2013年第1期49卷 119-136页

作者： Narimani, Mohammad Rasoul Azizipanah-Abarghooee, Rasoul Zoghdar-Moghadam-Shahrekohne, Behrouz Gholami, Kayvan Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Kermanshah Branch Kermanshah Iran Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran

This paper presents a new hybrid algorithm based on the Particle Swarm optimization (PSO) and the Shuffle Frog Leaping algorithms (SFLA) for solving the Optimal Power Flow (OFF) in power systems. In consequence of economical issues and increasing of the social welfare, the OPF problem is turning into a pretty remarkable problem and getting more and more important in power systems. The proposed optimization problem has considered the real conditions of power generation involving the prohibit zones, valve point effect and multi-fuel type of generation units. Increasing concerns over the environmental issues forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the OPF problem has become a multi-objective optimization problem. This paper takes advantages of the Pareto optimal solution and fuzzy decision making method in order to achieve the set of optimal solutions and best compromise solution, respectively. The presented algorithm is applied to 30, 57 and 118-bus test systems and the obtained results are compared with those in literature. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Optimal power flow Multi-objective Pareto optimal solution hybrid optimization algorithm Particle swarm optimization Shuffle frog leaping algorithm

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An efficient e-learning recommendation system for user preferences using hybrid optimization algorithm

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SOFT COMPUTING 2021年第14期25卷 9377-9388页

作者： Vedavathi, N. Anil Kumar, K. M. NIE Inst Technol Dept Comp Sci & Engn Mysore Karnataka India Sri Jayachamarajendra Coll Engn Dept Comp Sci & Engn Mysore Karnataka India

The expanding approval of e-learning structure has made the need for the customized suggestion prototype which can be utilized to advance the successful learning condition for the learners. Customized suggestion model is a particular sort of data separating framework used to recognize a lot of articles that are applicable to a e-learners. In this paper, we mainly propose the efficient e-learning recommendation (EELR) system for user preferences using hybrid optimization algorithm (HOA). EELR system constructs a HOA with deep recurrent neural network (DRNN) and improved whale optimization (IWO) algorithm. First, DRNN is utilized to order the e-learner types dependent on these e-learner gatherings, clients can acquire course proposal from the gathering's persuasion. Thereafter, the conduct and the inclinations of the learners are examined via completing the mining of the arrangements watched every now and again by the IWO calculation. Rather than a learner effectively looking for data, recommender frameworks give counsel to students about articles they may wish to analyze. At last, the proposal of the e-learning depends on the appraisals comparing to these arrangements watched often. This proposed system is going to implement and validate in numerous e-learning entries against the client inclinations over some undefined time frame and demonstrated to be more proficiency and exactness contrasted with the customary recommender framework. This strategy can help learners to grasp the knowledge system and learning direction, and improve their learning efficiency. Observation results show that the proposed methodology empowers the asset suggestion to singular clients, which is started from different sources.

关键词： Efficient e-learning recommendation hybrid optimization algorithm Deep recurrent neural network Improved whale optimization

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An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems

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INFORMATION SCIENCES 2021年 561卷 304-325页

作者： Pan, Jeng-Shyang Liu, Nengxian Chu, Shu-Chuan Lai, Taotao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve computationally expensive optimization problems. The critical idea of SAEAs is to combine the powerful searching capabilities of evolutionary algorithms with the predictive capabilities of surrogate models. In this study, an efficient surrogate-assisted hybrid optimization (SAHO) algorithm is proposed via combining two famous algorithms, namely, teaching-learning-based optimization (TLBO) and differential evolution (DE). The TLBO is focused on global exploration and the DE is concentrated on local exploitation. These two algorithms are carried out alternately when no better candidate solution can be found. Meanwhile, a new prescreening criterion based on the best and top collection information is introduced to choose promising candidates for real function evaluations. Besides, two evolution control (i.e., the generation-based and individual-based) strategies and a top-ranked restart strategy are integrated in the SAHO. Moreover, a local RBF surrogate which does not need too many training samples is employed to model the landscapes of the target function. Sixteen benchmark functions and the tension/compression spring design problem are adopted to compare the proposed SAHO with other state-of-the-art approaches. Extensive comparison results demonstrate that the proposed SAHO has superior performance for solving expensive optimization problems. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Surrogate-assisted hybrid optimization algorithm Teaching-learning-based optimization Differential evolution Expensive problems

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Sensor placement for structural health monitoring using hybrid optimization algorithm based on sensor distribution index and FE grids

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STRUCTURAL CONTROL & HEALTH MONITORING 2018年第6期25卷 e2160-e2160页

作者： Yang, Chen China Acad Space Technol Qian Xuesen Lab Space Technol Beijing 100094 Peoples R China

Considering the adjustments for sensor selections attributed by different finite element (FE) grids, a hybrid optimization algorithm including FE grids updating for optimal sensor placement is proposed based on effective independence method and sensor distribution index, in order to improve the algorithm efficient and reduce the redundancy information simultaneously. This study takes into account of the relationship of FE grids, redundancy information, and optimal sensor placement performance by detailed statements. Furthermore, in order to reduce the redundancy information caused by too fine FE grids and too near measurements, the proposed sensor distribution index can synthetically deal with both cases of the nearer nodes and overall sensor distribution ranges. Moreover, by means of normalization and weighted factor, the constituted fitness function, which consists of Fisher information and sensor distribution index, can be more competitive by eliminating the gap of orders caused by the different fitness functions. Therefore, the economical FE grids are suggested, and the corresponding optimum sensor locations are simultaneously obtained by the hybrid optimization algorithm. Finally, the proposed method is verified by a deployable antenna module of space solar power satellite and a wing of reusable launch vehicle, respectively.

关键词： effective independence method FE grids genetic algorithm hybrid optimization algorithm optimal sensor placement sensor distribution index

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Minimization of a Cogging Torque for an Interior Permanent Magnet Synchronous Machine using a Novel hybrid optimization algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2014年第3期9卷 859-865页

作者： Kim, Il-Woo Woo, Dong-Kyun Lim, Dong-Kuk Jung, Sang-Yong Lee, Cheol-Gyun Ro, Jong-Suk Jung, Hyun-Kyo Seoul Natl Univ Seoul South Korea Hyosung Corp Changwon Plant Special Motor Design Team Seoul South Korea Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Sungkyunkwan Univ Sch Elect & Elect Engn Seoul South Korea Dong Eui Univ Pusan South Korea

optimization of an electric machine is mainly a nonlinear multi-modal problem. For the optimization of the multi-modal problem, many function calls are required with much consumption of time. To address this problem, this paper proposes a novel hybrid algorithm in which function calls are less than conventional methods. Specifically, the proposed method uses the kriging metamodel and the fill-blank technique to find an approximated solution in a whole problem region. To increase the convergence speed in local peaks, a parallel gradient assisted simplex method is proposed and combined with the kriging meta-model. The correctness and usefulness of the proposed hybrid algorithm is verified through a mathematical test function and applied into the practical optimization as the cogging torque minimization for an interior permanent magnet synchronous machine.

关键词： hybrid optimization algorithm Kriging Motor optimization Permanent magnet machine Simplex

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