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A novel differential evolution algorithm integrating opposition-based learning and adjacent two generations hybrid competition for parameter selection of SVM

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EVOLVING SYSTEMS 2021年第1期12卷 207-215页

作者： Li, Jun Fang, Guokang Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China

Generalization performance of support vector machines (SVM) with Gaussian kernel is influenced by its model parameters, both the error penalty parameter and the Gaussian kernel parameter. The differential evolution (DE) algorithms have strong search ability and easy to implement. But it falls into local optimum easily. Hence a novel differential evolution algorithm which integrating opposition-based learning and hybrid competition between adjacent two generations is put forward for parameter selection of SVM (DGODE-SVM). In DGODE-SVM algorithm, opposition-based learning and hybrid competition between adjacent two generations are inserted into the differential evolution process. Nineteen experimental results on UCI datasets distinctly show that, compared with ODE-SVM, SaDE-SVM, DE-SVM, SVM, C4.5, KNN and NB algorithms, the proposed algorithm has higher classification accuracy.

关键词： Support vector machines differential evolution algorithm Opposition-based learning Adjacent two generations hybrid competition Parameter optimization

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evolutionary Model Selection and Parameter Estimation for Protein-Protein Interaction Network Based on differential evolution algorithm

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第3期12卷 622-631页

作者： Huang, Lei Liao, Li Wu, Cathy H. Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Univ Delaware Ctr Bioinformat & Computat Biol Newark DE 19711 USA

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network remains a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work, we developed a novel method based on Approximate Bayesian Computation and modified differential evolution algorithm (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms for PPI networks more accurately. We tested our method for its power in differentiating models and estimating parameters on simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show duplication attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks.

关键词： evolutionary models protein interaction networks differential evolution algorithm

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An Improved differential evolution algorithm for Optimal Location of Battery Swapping Stations Considering Multi-Type Electric Vehicle Scale evolution

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IEEE ACCESS 2019年 7卷 73020-73035页

作者： Wang, Shouxiang Yu, Lu Wu, Lei Dong, Yichao Wang, Hongkun Tianjin Univ Key Lab Minist Educ Tianjin 300072 Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA

Scientific scale forecasting of multi-type electric vehicles (EVs) is critical to accurately analyze the planning and operation of battery-swapping stations (BSSs) and charging stations (CSs). This paper predicts the proportions of plug-in electric vehicles (PEVs), hybrid electric vehicles (HEVs), and battery-swapping electric vehicles (BSEVs) in the total EV fleet in multi-scenarios via a system dynamics (SD) method. Relying on the predicted evolution scale of the BSEVs and the service demand of BSSs calculated by the service radius (SR) method, an improved differential evolutional algorithm combing with Monte Carlo searching (IDEA-MCS) method is proposed to obtain the optimal location of BSSs in a certain region in Beijing, which achieves an economic optimum of BSSs under the battery-swapping mode (BSM) via centralized charging and unified distribution (CCAUD). The analytical results show that the proportion of the BSEVs in different scenarios is the major driver that impacts the location of BSSs. The distribution of BSSs' BS demand in the optimistic scenario is more inhomogeneous than that in the other scenarios. In addition, a cross-comparison of optimal profits in different scenarios is conducted to verify the optimality of BSS locations for a given scenario. Finally, the proposed IDEA-MCS method is compared with the DEA method and IDEA method to verify its optimality.

关键词： Battery-swapping station (BSS) differential evolution algorithm optimal location system dynamics (SD) method

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Parameter identification of chaotic systems using improved differential evolution algorithm

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NONLINEAR DYNAMICS 2010年第1-2期61卷 29-41页

作者： Ho, Wen-Hsien Chou, Jyh-Horng Guo, Ching-Yi Natl Kaohsiung First Univ Sci & Technol Inst Syst Informat & Control Kaohsiung 824 Taiwan Kaohsiung Med Univ Dept Med Informat Management Kaohsiung 807 Taiwan

In this paper, an improved differential evolution algorithm, named the Taguchi-sliding-based differential evolution algorithm (TSBDEA), is proposed to solve the problem of parameter identification for Chen, Lu and Rossler chaotic systems. The TSBDEA, a powerful global numerical optimization method, combines the differential evolution algorithm (DEA) with the Taguchi-sliding-level method (TSLM). The TSLM is used as the crossover operation of the DEA. Then, the systematic reasoning ability of the TSLM is provided to select the better offspring to achieve the crossover, and consequently enhance the DEA. Therefore, the TSBDEA can be more robust, statistically sound, and quickly convergent. Three illustrative examples of parameter identification for Chen, Lu and Rossler chaotic systems are given to demonstrate the applicability of the proposed TSBDEA, and the computational experimental results show that the proposed TSBDEA not only can find optimal or close-to-optimal solutions but also can obtain both better and more robust results than the DEA.

关键词： differential evolution algorithm Taguchi sliding level method Chaotic systems

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An improved free search differential evolution algorithm: A case study on parameters identification of one diode equivalent circuit of a solar cell module

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ENERGY 2015年第Part2期93卷 1515-1522页

作者： Hultmann Ayala, Helon Vicente Coelho, Leandro dos Santos Mariani, Viviana Cocco Askarzadeh, Alireza Pontif Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS BR-80215901 Curitiba Parana Brazil Fed Univ Parana UFPR Dept Elect Engn BR-81531980 Curitiba Parana Brazil Pontif Catholic Univ Parana PUCPR Mech Engn Grad Program PPGEM BR-80215901 Curitiba Parana Brazil Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

The present paper deals with the parameter identification of one diode model equivalent circuit of solar cell modules from real data acquired in different temperature conditions. We termed this procedure as an optimization problem and solved it through the FSDE (Free Search differential evolution) algorithm as well as a novel IFSDE (Improved FSDE) approach. The IFSDE is compared with other well-known metaheuristics, namely genetic algorithms, harmony search and particle swarm optimization, showing overall better results for the proposed IFSDE approach. In particular, the IFSDE is better in escaping local optima and obtained better results. Identified results are compared with acquired data, what shows the validity of the proposed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： differential evolution algorithm Mathematical modeling Parameter identification Solar cells

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Improved differential evolution algorithm for nonlinear programming and engineering design problems

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NEUROCOMPUTING 2015年 148卷 628-640页

作者： Tsai, Jinn-Tsong Natl Pingtung Univ Dept Comp Sci Pingtung 900 Taiwan

An improved differential evolution algorithm (IDEA) is proposed to solve nonlinear programming and engineering design problems. The proposed IDEA combines the Taguchi method with sliding levels and a differential evolution algorithm (DEA). The DEA has a powerful global exploration capability on macrospace and uses fewer control parameters. The systematic reasoning ability of the orthogonal array with sliding level and response table is used to exploit the better individuals on microspace to be potential offspring. Therefore, the proposed IDEA is well enhanced and balanced on exploration and exploitation. In this study, the sensitivity of evolutionary parameters for the performance of the IDEA is explored, and the IDEA shows its effectiveness and robustness compared with both the DEA and the real-coded genetic algorithm. The engineering design problems usually encounter a large number of design variables, a mix type of both discrete and continuous design variables, and many design constraints. The proposed IDEA is used to solve these engineering design optimization problems, and demonstrates its capability, feasibility, and robustness. From the computational experiments, the introduced IDEA can obtain better results and more prominent performance than the methods presented in the literatures. (C) 2014 Elsevier B.V. All rights reserved.

关键词： differential evolution algorithm Taguchi method Sliding level Engineering design problem

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Peak-to-Average Power Ratio Reduction in Lifting Based Wavelet Packet Modulation Systems Using differential evolution algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2017年第3期94卷 1073-1086页

作者： Bozkurt, Yuksel Tokur Taspinar, Necmi Gaziantep Univ Vocat Sch Tech Sci Dept Elect & Automat Gaziantep Turkey Erciyes Univ Dept Elect & Elect Engn Kayseri Turkey

Integer wavelet transform (IWT) is an alternative to fast Fourier transform (FFT) in orthogonal frequency division multiplexing (OFDM) systems. It serves implementation flexibility and performance improvement by diminishing high side lobes, sensitivity to time and frequency synchronization. However, high PAPR of the transmitted signals is also a significant problem for IWT as in FFT based OFDM. One of the methods used for reducing the peak-to-average power ratio (PAPR) is partial transmit sequences (PTS) and this method requires an exhaustive search to obtain optimum reduction. This study examines the PAPR reduction potential of differential evolution algorithm based PTS scheme in lifting based wavelet packet modulation (LBWPM) system. The simulation results indicate that remarkable PAPR reduction performance with a low computational load for LBWPM system can be achieved using the proposed differential evolution based PTS scheme.

关键词： differential evolution algorithm Lifting based wavelet packet modulation Partial transmit sequences Peak-to-average power ratio

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A novel differential evolution algorithm for joint replenishment problem under interdependence and its application

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2012年第1期135卷 190-198页

作者： Wang, Lin He, Jing Wu, Desheng Zeng, Yu-Rong Univ Toronto RiskLab Toronto ON M5S 3G3 Canada Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Hubei Univ Econ Sch Informat Management Wuhan 430205 Peoples R China

In this paper, we propose a new differential evolution (DE) algorithm for joint replenishment of inventory using both direct grouping and indirect grouping which allows for the interdependence of minor ordering costs. Since solutions to the joint replenishment problem (JRP) can be represented by integer decision variables, this makes the JRP a good candidate for the DE algorithm. The results of testing randomly generated problems in contrastive numerical examples and two extended experiments show that the DE algorithm provides close to optimal results for some problems than the evolutionary algorithm (EA), which has been proved to be an efficient algorithm. Moreover, the DE algorithm is faster than the EA for most problems. We also conducted a case study and application results suggest that the proposed model is successful in decreasing total costs of maintenance materials inventories significantly in two power companies. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Joint replenishment Interdependence Minor ordering costs differential evolution algorithm

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A modified Hammerstein modeling by the differential evolution algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第6-7期18卷 5099-5112页

作者： Chang, Wei-Der Shu Te Univ Dept Comp & Commun Kaohsiung 824 Taiwan

This paper focuses on the nonlinear system modeling based on using a modified Hammerstein system model. The proposed Hammerstein structure is composed of a bilinear neural network (BNN) and a recursive digital system in the cascaded form. The former is taken to be the nonlinear function part of the Hammerstein model, and the latter is used as the linear dynamic subsystem. The BNN is then constructed by the bilinear digital system and the recurrent neural network, which already possesses a satisfactory modeling capacity. To update all of adjustable parameters within the proposed Hammerstein model, a popular and powerful evolutionary computation called the differential evolution (DE) is utilized so that the model output can be closely to the actual nonlinear system output. Finally, a simulated nonlinear chemical process system, continuously stirred tank reactor (CSTR), is illustrated with the modeling phase and testing phase. Some experiment results as compared with another method from the subject literature are provided to demonstrate the feasibility of the proposed method and its good modeling.

关键词： Bilinear neural network Recursive digital system Hammerstein model differential evolution algorithm System modeling

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L∞ Metric based Multi-Objective differential evolution algorithm and its Industrial Application

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2022年第3期38卷 645-663页

作者： Guo, Zhan Ersoy, Okan K. Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200237 Peoples R China

In multi-objective optimization problems, the objective space of fitness functions has a close relationship with the solution space. Extracting the optimal direction and optimal parameter information are very useful for the optimization process. This paper proposes multi-objective differential evolution algorithm with a clustering based objective space division and parameter adaptation (MODECD). L. metric matrix based optimal strategy is used to split the objective space into sub-spaces and to extract the optimal directions. A fitness value based parameter adaptation and mutation strategy are used to extract the optimal strategy information. The results with 20 benchmark tests show the competitiveness of the MODECD algorithm in both convergence speed and diversity of solution approximating the Pareto front. In addition, MODECD is used to optimize the fermentation process of sodium gluconate as an example of its superior performance in solving real-world problems.

关键词： differential evolution algorithm L-infinity metric parameter adaptation multi-objective optimization sodium gluconate production

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