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Research on the parameter inversion problem of prestack seismic data based on improved differential evolution algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2017年第4期20卷 2881-2890页

作者： Wu, Qinghua Zhu, Zhixin Yan, Xuesong WuHan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

The parameter inversion technology composed by intelligent algorithm and AVO inversion for prestack seismic data provides a comparatively effective identification method for oil-gas exploration. However, traditionally intelligent iterative algorithm, such as, genetic algorithm, shows many disadvantages in solving this problem, including highly depending on initial model, fast convergence in algorithm and being easy to fall into local optimal. Therefore, an unsatisfied inversion performance is produced. In order to solve the above problems, this paper proposes a parameter inversion method based on improved differential evolution algorithm which is better in solving parameter inversion problems of prestack seismic data. In the proposed algorithm, aims at the Aki and Rechard approximation formula used specific initialization strategy, then the initialization parameter curve more smooth. Otherwise, the new algorithm has many advantages, such as, fast computing speed, simple operation, a low independence to initial model and good global convergence, this algorithm is the right choice in solving the parameter inversion problem based on pre-stack seismic data of real number encoding.

关键词： differential evolution algorithm Prestack seismic data Parameter inversion initialization strategy

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Multi-objective optimum design of five-phase squirrel cage induction motor by differential evolution algorithm

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ENERGY REPORTS 2022年 8卷 51-62页

作者： Chen, Hansi Zhao, Jinghong Wang, Hanming Zhang, Qingfei Luo, Xiangyu Xu, Hao Xiong, Yiyong Naval Univ Engn Coll Elect Engn Wuhan 430033 Hubei Peoples R China

Compared with the traditional induction motors, five-phase squirrel cage induction motor (FSCIM) have the advantages of lower torque ripple and single-phase power, flexible control strategy, higher power density and fault-tolerant operation, which has been widely applied on electrical vehicles, rail transit, underwater vehicles and so on. This paper presents an analytical design optimization method based on the coupling of electromagnetic equivalent circuit (EEC) and differential evolution algorithm (DEA). Furthermore, the difference between three and five-phase winding on harmonic specific leakage permeance is investigated in detail. The nonlinear and oeolotropic influences of design geometrical parameters (slots openings, main dimension ratio, yoke height, airgap length, etc.) on the performances of the basic FSCIM model are evaluated with analytical model. For more accurate results, the harmonic characteristics of five-phase winding structure, iron core saturation, rotor slot skew width, stator end winding, slot leakage inductance and stator and rotor slot structure are considered. Meanwhile, the efficiency, power factor, maximum torque, slot fill factor and material consumption of FSCIM are calculated as the comprehensive objective function. The transient finite element analysis (TFEA) and experimental test verify the accuracy of the proposed optimum model. In addition, the feasibility of the multi objective optimization design scheme provided by the analytical model is verified by TFEA. (c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Five-phase induction motor differential evolution algorithm Multi objective optimization Fast performance computation

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Kinship-based differential evolution algorithm for unconstrained numerical optimization

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NONLINEAR DYNAMICS 2020年第2期99卷 1341-1361页

作者： Formica, Giovanni Milicchio, Franco Roma Tre Univ Dipartimento Architettura Via Madonna Monti 40 Rome Italy Roma Tre Univ Dipartimento Ingn Via Vasca Navale 79 Rome Italy

We propose a modification of the standard differential evolution (DE) algorithm in order to significantly make easier and more efficient standard DE implementations. Taking advantages from chaotic map approaches, recently proposed and successfully implemented for swarm intelligence-based algorithms, our DE improvement facilitates the search for the best population and then the optimal solution. More specifically, we work with a genetic memory that stores parents and grandparents of each individual (its kin) of the population generated by the DE algorithm. In this way, the new population is carried out not only on the basis of the best fitness of a certain individual, but also according to a good score of its kin. Additionally, we carried out a wide numerical campaign in order to assess the performances of our approach and validated the results with standard statistical techniques.

关键词： Optimization differential evolution algorithm Chaotic maps

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A new approach for wind turbine placement problem using modified differential evolution algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第6期27卷 4659-4672页

作者： Hakli, Huseyin Necmettin Erbakan Univ Dept Comp Engn Fac Engn & Architecture Konya Turkey

Energy use is increasing worldwide with industrialization and advancing technology. Following this increase, renewable energy resources are increasingly preferred to reduce the costs of energy production. Wind energy is preferred as a renewable energy resource because it is clean and safe. Wind turbines are used to meet the demand for wind energy. They are placed close to each other to generate higher amounts of energy. However, the wake effect problem arises in these types of layouts, and this hinders the turbines from producing the desired yield. A modified differential evolution (MDE) algorithm was proposed in this study to solve the placement problem for wind turbines, and employed a binary-real-coded method - obtained by combining binary coding and real coding. The proposed method contains three different modifications: generation of the initial population, regeneration, and mutation. The effective distribution of the wind turbines on land was achieved with a preliminary operation proposed to generate the initial population. In addition, with the MDE method, population regeneration and elitism were carried out to increase the diversity of population and to preserve the success of the method. Finally, a mutation operation was performed on the individuals to alternate the presence or absence of wind turbines. To investigate the performance of the MDE method in solving the wind turbine placement problem, the method was applied to a study area of 2 x 2 km. The results were compared with those obtained with other methods used in the published literature for the wind turbine placement problem. The most successful and productive placement was achieved using the proposed method, and experimental results showed that the MDE is an efficient and successful tool to solve the wind turbine placement problem.

关键词： Wind turbine placement binary-real coding differential evolution algorithm optimization

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An efficient multiobjective differential evolution algorithm for engineering design

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2009年第2期38卷 137-157页

作者： Gong, Wenyin Cai, Zhihua Zhu, Li China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive epsilon-dominance (pa epsilon-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.

关键词： Engineering design Multiobjective optimization differential evolution algorithm Orthogonal design method Pareto-adaptive epsilon-dominance Constraint-handling method

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Online algorithm configuration for differential evolution algorithm

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APPLIED INTELLIGENCE 2022年第8期52卷 9193-9211页

作者： Huang, Changwu Bai, Hao Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China INSA Rouen Normandie Lab Mech Normandy LMN F-76000 Rouen France

The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm's configuration, including the operators and parameter values for maximizing the algorithm's performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC's hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE's performance.

关键词： Automatic algorithm configuration differential evolution algorithm Adaptive parameter control Multi-armed bandit Kernel density estimation Machine learning

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A Novel Mutual Fractional Grey Bernoulli Model With differential evolution algorithm and Its Application in Education Investment Forecasting in China

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IEEE ACCESS 2020年 8卷 97839-97850页

作者： Xie, Wanli Pu, Bin Pei, Chunying Lee, Shin-Jye Kang, Yan Nanjing Normal Univ Inst EduInfo Sci & Engn Nanjing 210097 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410006 Peoples R China Piesat Informat Technol Co Ltd Aerosp Piesat Res Inst Beijing 100048 Peoples R China Natl Chiao Tung Univ Inst Technol Management Hsinchu 30010 Taiwan Yunnan Univ Natl Pilot Sch Software Kunming 650091 Yunnan Peoples R China

Nowadays, the nonlinear grey Bernoulli model [NGBM(1,1)] has been successfully applied to various fields. Its main advantage is that the power exponent can better reflect the non-linear characteristics of the original data. However, the parameters of the model (i.e., the order of accumulation, coefficient of background value, and power index) must be optimized to fit the development law of the system. In this study, a fractional non-linear grey Bernoulli model [MFNGBM (1,1)] is proposed to reduce the perturbation limit of the classical NGBM and further improve the accuracy of the model, which uses mutual fractional operators and a new optimization scheme with a differential evolution (DE) algorithm for forecasting education investment. In the scheme, the power exponent of the Bernoulli differential equation, coefficient of background, and cumulative order of the original sequence are taken as decision variables, and their optimal parameters obtained by iteratively adjusting fitness functions. The experimental evaluation is conducted on two types of open-source data, and the results show that the proposed method can be very competitive with the popular baselines. Finally, MFNGBM(1,1) is used to predict China's education investment in 2020-2025.

关键词： Predictive models Mathematical model Investment Education Forecasting Indexes Optimization Grey system grey forecasting model non-linear grey bernoulli model fractional order differential evolution algorithm educational investment

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Finite Markov chain analysis of classical differential evolution algorithm

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2014年 268卷 121-134页

作者： Hu, ZhongBo Xiong, ShengWu Su, QingHua Fang, ZhiXiang Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan 432000 Hunan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China

Theoretical analyses of algorithms are important to understand their search behaviors and develop more efficient algorithms. Compared with the plethora of works concerning the empirical study of the differential evolution (DE), little theoretical research has been done to investigate the convergence properties of DE so far. This paper focuses on theoretical researches on the convergence of DE and presents a convergent DE algorithm. First of all, it is proved that the classical DE cannot converge to the global optimal set with probability 1 by using the property that it cannot escape from a local optimal set. Inspired by the characteristics of the elitist genetic algorithm, this paper proposed a modified DE to overcome the disadvantage. The proposed algorithm employs two operators that assist it in escaping from a local optimal set and enhance the diversity of the population. And it is then verified that the proposed algorithm is capable of converging to global optima with probability 1. The theoretical research of this paper is undertaken in a finite discrete set, and the analysis tool used is the Markov chain. The numerical experiments are conducted on a deceptive function and a set of benchmark functions. The experimental results support the theoretical analyses on the convergence performances of the classical and modified DE algorithm. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Convergence in probability Continuous optimization differential evolution algorithm Markov chain

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Back propagation neural network with adaptive differential evolution algorithm for time series forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第2期42卷 855-863页

作者： Wang, Lin Zeng, Yi Chen, Tao Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430074 Peoples R China

The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE-BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE-BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Time series forecasting Back propagation neural network differential evolution algorithm

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Optimal power flow using differential evolution algorithm

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ELECTRICAL ENGINEERING 2009年第2期91卷 69-78页

作者： Abou El Ela, A. A. Abido, M. A. Spea, S. R. Menoufia Univ Dept Elect Engn Fac Engn Shibin Al Kawm Egypt King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution algorithm for optimal settings of OPF problem control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results are compared with the results reported in the literature. The results show the effectiveness and robustness of the proposed approach.

关键词： Optimal power flow differential evolution algorithm Fuel cost minimization Voltage profile improvement Voltage stability enhancement

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