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A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization

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APPLIED MATHEMATICS AND COMPUTATION 2014年 234卷 452-459页

作者： Coelho, Leandro dos Santos Hultmann Ayala, Helon Vicente Mariani, Viviana Cocco Pontif Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS BR-80215901 Curitiba Parana Brazil Univ Fed Parana UFPR Dept Elect Engn BR-81531980 Curitiba Parana Brazil Pontif Catholic Univ Parana PPGEM Mech Engn Grad Program BR-80215901 Curitiba Parana Brazil

evolutionary algorithms (EAs) have yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization problems. Due to its population-based nature, EAs can avoid being trapped in a local optimum, and consequently have the ability to find global optimal solutions. As a novel evolutionary technique, differential evolution (DE) has received increasing attention and wide applications in a variety of fields. DE algorithm uses an efficient way of self-adapting mutation using small populations for function optimization over continuous spaces. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution, and robustness. In this paper, an effective self-adaptive DE algorithm based on Gaussian probability distribution, gamma distribution and chaotic sequence (DEGC) for solving continuous global optimization problems is proposed. The proposed DEGC algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with a classical DE approach and a self-adaptive DE approach demonstrate the effectiveness and efficiency of the proposed DEGC algorithm. (C) 2014 Elsevier Inc. All rights reserved.

关键词： evolutionary algorithms differential evolution algorithm Chaotic sequence Global optimization

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Flow Control in Network Media Information Transmission Based on differential evolution algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期127卷 1265-1282页

作者： Liu, Libin Sun, Zhiyuan Shaanxi Radio & Televis Univ Sch Publ Adm Xian 710068 Peoples R China Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China

In order to solve the problems of transmission delay and data loss caused by network congestion and strengthen the quality of service of transmission delay and throughput, a method of flow control in network media information transmission based on differential evolution algorithm was presented. By analyzing the delay effect of buffer on network instability, the problems of flow control at the receiving end were clarified. Moreover, the jitter between packet and end-to-end time delay parameters in dynamic caching mechanism were analyzed. The maximum and minimum values were set to calculate the balance point of real-time communication and smooth network information transmission flow. In addition, the differential evolution algorithm based on adaptive quadratic mutation of population fitness variance was used to improve the adaptive global optimization structure, organize the minimum evolution and input the minimum sample. Through reasonable selection of controller parameters and adaptive calculation, the poles of the closed-loop control system were obtained. Finally, the transmission performance index could be optimized. Thus, the stable control of transmission flow was completed. Simulation results show that the proposed algorithm can effectively improve the transmission performance of heterogeneous networks. Meanwhile, it has obvious advantages when the asymmetric parameters are high.

关键词： differential evolution algorithm Flow control Transmission delay Short-wave data

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Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified differential evolution algorithm

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Chinese Journal of Mechanical Engineering 2015年第4期28卷 702-715页

作者： WANG Congzhe FANG Yuefa GUO Sheng School of Mechanical Electronic and Control Engineering Beijing Jiaotong University

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified differential evolution（DE） algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

关键词： ankle rehabilitation parallel robot multi-objective optimization differential evolution algorithm

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Temporal-spatial analysis of water environmental capacity based on the couple of SWAT model and differential evolution algorithm

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JOURNAL OF HYDROLOGY 2019年 569卷 155-166页

作者： Wang, Qingrui Liu, Ruimin Men, Cong Guo, Lijia Miao, Yuexi Beijing Normal Univ State Key Lab Water Environm Simulat Sch Environm 19 Xinjiekouwai St Beijing 100875 Peoples R China

The objective of this study was to advance a new method to dynamically analyze the water environmental capacity (WEC) of non-point source (NPS) pollution. A Soil and Water Assessment Tool (SWAT) model and differential evolution (DE) algorithm were coupled to automatically provide a calculation of the WEC in a daily step. The results showed that the largest ideal WECs occurred in the wet season with values up to 4500 t and 80 t for total nitrogen (TN) and total phosphorus (TP), respectively. The ideal WEC had a positive correlation with the rainfall intensity, while the opposite was true for actual WEC. The smallest value of actual WEC for TN was -150 tin June, and a negative value also occurred in June for TP. This is because NPS pollution discharge was more sensitive to rainfall and flow change than the ideal WEC. Compared to phosphorus, the WEC of nitrogen was more affected by the hydrological and NPS pollution discharge conditions. In addition, the WEC of nitrogen and phosphorus in organic species was more affected by seasonal change than that of nitrogen and phosphorus in inorganic species. Spatially, the channels in the middle and southeast of the study area had the smallest actual WEC and even negative values were observed. The results of remnant WEC showed that the smallest value occurred in July and June with values of - 800 t and -140 t for TN and TP, respectively. Thus, significant attention should be paid to controlling NPS pollution during the wet season, and organic nitrogen and phosphorus should be set as the priority pollutants to control. Furthermore, controlling the NPS pollution discharge and enhancing the WEC should be carried out simultaneously. Compared to previously used methods, the calculation method employed in this study had the ability to simulate the transformation and interaction between nitrogen and phosphorus in organic and inorganic species, and better considered dynamic changes in the input conditions.

关键词： Water environmental capacity Non-point source pollution Temporal-spatial analysis SWAT model differential evolution algorithm

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Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO)

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COMPUTERS & OPERATIONS RESEARCH 2011年第8期38卷 1188-1198页

作者： Boussaid, Ilhem Chatterjee, Amitava Siarry, Patrick Ahmed-Nacer, Mohamed Univ Paris Est Creteil Val de Marne LiSSi EA 3956 F-94010 Creteil France Univ Sci & Technol Houari Boumediene Dept Elect Engn & Comp Sci Algiers 16111 Algeria Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

The present paper proposes a new stochastic optimization algorithm as a hybridization of a relatively recent stochastic optimization algorithm, called biogeography-based optimization (BBO) with the differential evolution (DE) algorithm. This combination incorporates DE algorithm into the optimization procedure of BBO with an attempt to incorporate diversity to overcome stagnation at local optima. We also propose to implement an additional selection procedure for BBO, which preserves fitter habitats for subsequent generations. The proposed variation of BBO, named DBBO, is tested for several benchmark function optimization problems. The results show that DBBO can significantly outperform the basic BBO algorithm and can mostly emerge as the best solution providing algorithm among competing BBO and DE algorithms. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Biogeography-based optimization differential evolution algorithm

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Using a Grey model optimized by differential evolution algorithm to forecast the per capita annual net income of rural households in China

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2012年第5期40卷 525-532页

作者： Zhao, Ze Wang, Jianzhou Zhao, Jing Su, Zhongyue Lanzhou Univ Sch Math & Stat Lanzhou 730000 Gansu Peoples R China

China is a major developing country where farmers account for over 57% of the population. Thus, promoting a rural economy is crucial if the Chinese government is to improve the quality of life of the nation as a whole. To frame scientific and effective rural policy or economic plans, it is useful and necessary for the government to predict the income of rural households. However, making such a prediction is challenging because rural households income is influenced by many factors, such as natural disasters. Based on the Grey Theory and the differential evolution (DE) algorithm, this study first developed a high-precision hybrid model, DE-GM(1,1) to forecast the per capita annual net income of rural households in China. By applying the DE algorithm to the optimization of the parameter lambda. which was generally set equal to 0.5 in GM( 1.1), we obtained more accurate forecasting results. Furthermore, the DE-Rolling-GM(1,1) was constructed by introducing the Rolling Mechanism. By analyzing the historical data of per capita annual net income of rural households in China from 1991 to 2008, we found that DE-Rolling-GM(1,1) can significantly improve the prediction precision when compared to traditional models. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： GM(1,1) Parameter optimization differential evolution algorithm Rolling Mechanism

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Economic-effectiveness analysis of micro-fins helically coiled tube heat exchanger and optimization based on multi-objective differential evolution algorithm

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APPLIED THERMAL ENGINEERING 2022年第PartB期201卷 117764-117764页

作者： Yuan, Yuyang Cao, Jiaming Wang, Xuesheng Zhang, Zhao Liu, Yanbin East China Univ Sci & Technol Key Lab Pressure Syst & Safety Minist Educ Shanghai 200237 Peoples R China

In this study, the heat transfer characteristics and hydrodynamics of a new type of micro-fin helically coiled tube (MF-HCT) are modelled and compared with those of typical smooth helically coiled tubes (HCTs). Calculation models are developed and compared for both helically coiled tube heat exchangers (HCTHEXs) and micro-fin helically coiled tube heat exchangers (MF-HCTHEXs). The total cost and effectiveness are selected as the objective functions, and five independent structural parameters are selected for each type of HCTHEX. Subsequently, an adaptive multi-objective optimization differential evolution algorithm is implemented for both of them, and the results are compared. The results indicate that, for Reynolds numbers smaller than 22,000, the MFHCT performs better than the HCT, and the total cost of the MF-HCTHEX is lower than that of the HCTHEX under the same effectiveness. The difference between their total cost increases with their effectiveness and can be up to 79,000 euro. For both the HCTHEX and MF-HCTHEX, when the effectiveness is less than 0.9, it is more appropriate to select a larger winding angle, layer spacing, and tube spacing.

关键词： Helically coiled tube heat exchanger Multi-objective optimization Economic analysis Effectiveness differential evolution algorithm Heat transfer

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Fusion of multi-focus images using differential evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第12期37卷 8861-8870页

作者： Aslantas, V. Kurban, R. Erciyes Univ Fac Engn Dept Comp Engn TR-38039 Kayseri Turkey

In an image captured by a CCD/CMOS sensor with an optical lens, only the objects within the depth of field are sharply focused. One way to get an everywhere-in-focus image is to fuse the images of the same scene which are taken with different focal settings. In this paper, a novel optimal method for multi-focus image fusion using differential evolution algorithm is presented. The source images are first decomposed into blocks. Then, the sharper blocks are selected by employing a sharpness criterion function. The selected blocks are finally combined to construct the fused image. The motivation of the proposed method lies in the fact that an optimized block size could be more effective than a fixed block size. Experimental results show that the proposed method outperforms other traditional methods and genetic algorithm based method in terms of both quantitative and visual evaluations. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Multi-focus image fusion Extended depth of field Sharpness metrics Genetic algorithm differential evolution algorithm

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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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