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The defect of the Grey Wolf optimization algorithm and its verification method

KNOWLEDGE-BASED SYSTEMS 2019年第May 1期171卷 37-43页

作者： Niu, Peifeng Niu, Songpeng Liu, Nan Chang, Lingfang Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China

Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function's optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic Gray Wolf optimization algorithm optimization algorithm Defect New verification method

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optimization algorithm for rapid 3D gravity inversion

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Applied Geophysics 2019年第4期16卷 507-518,561页

作者： Jing Lei Yao Chang-Li Yang Ya-Bin Xu Meng-Long Zhang Guang-Zhi Ji Ruo-Ye School of geophysics and information technology China University of Geosciences(Beijing)Beijing 100083China The National Center for Geological Exploration Technology Langfang 065000China Institute of Geophysical and Geochemical Exploration Chinese Academy of Geological SciencesLangfang 065000China.

The practical application of 3D inversion of gravity data requires a lot of computation time and storage *** solve this problem,we present an integrated optimization algorithm with the following components:(1)targeting high accuracy in the space domain and fast computation in the wavenumber domain,we design a fast 3D forward algorithm with high precision;and(2)taking advantage of the symmetry of the inversion matrix,the main calculation in gravity conjugate gradient inversion is decomposed into two forward calculations,thus optimizing the computational efficiency of 3D gravity *** verify the calculation accuracy and efficiency of the optimization algorithm by testing various grid-number models through numerical simulation experiments.

关键词： Gravity 3D inversion optimization algorithm high efficiency massive data

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A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach

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SOFT COMPUTING 2019年第6期23卷 1901-1916页

作者： Selvi, C. Sivasankar, E. Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India

Recommender system (RS) is an emerging technique in information retrieval to handle a large amount of online data effectively. It provides recommendation to the online user in order to achieve their correct decisions on items/services quickly and easily. Collaborative filtering (CF) is one of the key approaches for RS that generates recommendation to the online user based on the rating similarity with other users. Unsupervised clustering is a class of model-based CF, which is more preferable because it provides the simple and effective recommendation. This class of CF suffers by higher error rate and takes more iterations for convergence. This study proposes a modified fuzzy c-means clustering approach to eliminate these issues. A novel modified cuckoo search (MCS) algorithm is proposed to optimize the data points in each cluster that provides an effective recommendation. The performance of proposed RS is measured by conducting experimental analysis on benchmark MovieLens dataset. To show the effectiveness of proposed MCS algorithm, the results are compared with popular optimization algorithms, namely particle swarm optimization and cuckoo search, using benchmark optimization functions.

关键词： Recommender system (RS) Collaborative filtering (CF) Clustering approach optimization algorithm Modified fuzzy c-means (MFCM) Modified cuckoo search (MCS)

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Research on identification of irrational fractional-order systems in frequency domain based on optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第15期41卷 4351-4357页

作者： Chen Lanfeng Xue Dingyu Northeastern Univ Sch Informat Sci & Engn Shenyang Liaoning Peoples R China Shenyang Normal Univ Coll Phys Sci & Technol Shenyang Liaoning Peoples R China

Fractional-order calculus can obtain better results than the integer-order in control theory, so it has become a research hotspot in recent years. However, the structure of the irrational fractional-order system is complex, so its theoretical analysis and controller design are more difficult. In this paper, a method based on convolution integral is proposed to obtain the frequency domain response of the irrational model. Combined with the optimization algorithm, the model parameters are identified. Moreover, the rationalization of the irrational model is realized, which facilitates the analysis and application design of this kind models. Finally, two examples are given to illustrate the effectiveness and feasibility of the method by identifying parameters and rationalization.

关键词： Irrational fractional-order system convolution integral optimization algorithm model parameters rationalization of the irrational model

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Parameter Identification of Single-Phase Inverter Based on Improved Moth Flame optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第4-5期47卷 456-469页

作者： Wu, Zhongqiang Shen, Dandan Shang, Mengyao Qi, Songqi Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

By identifying the parameters of electronic circuit, parametric fault diagnosis of power electronic circuits can be realized. Many intelligent optimization algorithms are used to identify the parameters of electronic circuit, but most of them have the defects of slow convergence rate and easy to fall into local minimum. Moth flame optimization algorithm is a novel swarm intelligence bionic algorithm based on the intelligence behavior of moth positioning, which also has the above drawbacks. In order to improve the performance of algorithm, when updating the moth position, moth firstly moves in a straight line to the optimal position, then Levy flight is added. The improved algorithm improves the global optimization ability and accelerates the convergence speed. The improved moth flame optimization algorithm is applied for the parameter identification of single-phase inverter. The identification result is compared with the results of the other optimization techniques. The effectiveness and superiority of the improved algorithm are verified.

关键词： parameter identification fault diagnosis optimization algorithm single-phase inverter transfer function model moth flame optimization algorithm particle swarm optimization genetic algorithm Fast Fourier transform Levy flight

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A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran

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METHODSX 2021年 8卷 101226页

作者： Jalaee, Sayyed Abdolmajid Shakibaei, Alireza Horry, Hamid Reza Akbarifard, Hossein GhasemiNejad, Amin Robati, Fateme Nazari Zarin, Naeeme Amani Shahid Bahonar Univ Kerman Fac Management & Econ Dept Econ Kerman Iran

Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974-2013) and partly for testing the models (2014-2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process. (C) 20

关键词： Metaheuristic method Money demand Monetary and fiscal policies optimization algorithm

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A novel hybrid method based on Cuckoo optimization algorithm and artificial neural network to forecast world's carbon dioxide emission

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METHODSX 2021年 8卷 101310页

作者： Jalaee, Sayyed Abdolmajid Shakibaei, Alireza Akbarifard, Hossein Horry, Hamid Reza GhasemiNejad, Amin Robati, Fateme Nazari Zarin, Naeeme Amani Derakhshani, Reza Shahid Bahonar Univ Kerman Fac Econ & Management Dept Econ Kerman Iran Shahid Bahonar Univ Kerman Dept Geol Kerman Iran Univ Utrecht Dept Earth Sci Utrecht Netherlands

This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo optimization algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases. The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Global climate changes Metaheuristic method optimization algorithm

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An improved phase field method by using statistical learning theory-based optimization algorithm for simulation of martensitic transformation in NiTi alloy

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COMPUTATIONAL MATERIALS SCIENCE 2020年 172卷 109292-000页

作者： Wei, C. Ke, C. B. Liang, S. B. Cao, S. Ma, H. T. Zhang, X. P. South China Univ Technol Sch Mat Sci & Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510640 Guangdong Peoples R China Guangzhou Univ Earthquake Engn Res & Test Ctr Guangzhou Guangdong Peoples R China

An improved phase field method by using statistical learning theory based optimization algorithm is developed for solving the phase field equations through building simple relationships between the key phase field variables and the phase evolution driving force, and using statistical analysis of mass computed data during phase field simulation. Phase field simulation results of growth of R phase and the B2-R phase transformation in a Ni-rich Ni50.5Ti49.5 alloy by using the proposed statistical strategy algorithm are compared with that using the conventional numerical algorithm, which demonstrates that with coupling the statistical learning theory, i.e., by means of the optimization algorithm, the credible simulated microstructure is obtained while maintaining high accuracy, and meanwhile the computational time has been significantly reduced.

关键词： Martensitic transformation Phase field modeling optimization algorithm Statistical learning theory

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Virtual reconstruction of random moving image capturing points based on chaos embedded particle swarm optimization algorithm

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MICROPROCESSORS AND MICROSYSTEMS 2020年 75卷 103069-103069页

作者： Qu, Chen Northeast Normal Univ Changchun 130024 Jilin Peoples R China

During the exercise process, such as jumping, and running, the angles of different joints of the athlete's body will produce different changes, resulting in obvious mismatches in different joint feature points. The chaotic matching error makes the calculation results of the basic shape features of the athletes in different parts have large deviations. In this paper, an improved algorithm of CEPS (Chaos embedded particle swarm) optimization algorithm interpolation is introduced, which can improve the interpolation precision and reduce the calculation time of interpolation. Through interpolation, the fast Fourier transform can be used to realize the fast reconstruction of the target 3d image based on biomechanical characteristics. The experimental and simulation results show that the three-dimensional image reconstruction algorithm based on the interpolation method can reconstruct the three-dimensional image of the target, achieve the detection of the target and have good resolution, and improve the authenticity of the human motion image sequence three-dimensional dynamic simulation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Chaos embedded particle swarm optimization algorithm Chaos error control Image capturing points Virtual reconstruction Football athletes

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TLBO optimization algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

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TRAITEMENT DU SIGNAL 2020年第4期37卷 541-553页

作者： Ouali, Mohammed Assam Ghanai, Mouna Chafaa, Kheireddine Univ Msila Fac Technol Elect Dept LAAS Lab Msila 28000 Algeria Caen Univ GREYC Lab F-14000 Caen France Univ Batna 2 Elect Dept LAAAS Lab Fac Technol Batna 5000 Algeria

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals;these included real and artificial noise. For comparison purposes, both model and non-model-based methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

关键词： ECG signal ECG denoising type-2 fuzzy logic optimization algorithm TLBO

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