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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 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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Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on differential evolution algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年第Dec.期73卷 1015-1024页

作者： Moradi, Mohammad H. Abedini, Mohammad Tousi, S. M. Reza Hosseinian, S. Mandi Bu Ali Sina Univ Dept Elect Engn Fac Engn Hamadan Iran Bu Ali Sina Univ Dept Civil Engn Fac Engn Hamadan Iran

Electric Vehicles (EVs) are seen to have some negative impacts on microgrid performance, such as diminishing power quality and efficiency and increasing power losses, voltage variations and even customer energy prices. This paper proposes a new method for evaluating the effect of integrating a large number of EVs on a power system and their impact on the network voltage profile via injecting reactive power into highly-loaded buses. A multi-objective optimization problem is developed to obtain the optimal siting and sizing of charging stations and renewable energy sources (RES). The optimization problem focuses on reducing power losses, improving voltage stability of the system and reducing charging costs of EVs. In order to increase the network load factor some coefficients are introduced. Such coefficients, which depend on wind speed, solar irradiance and hourly peak demand ratio in the load characteristic of day-ahead, help aggregators to charge their EVs in off-peak hours. differential evolution (DE) algorithm is used for solving the optimization problem. The performance of the proposed method is evaluated for 69-bus and 94-bus microgrids. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： differential evolution algorithm Electric vehicle charging stations Renewable energy sources Autonomous microgrid

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A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems

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APPLIED INTELLIGENCE 2015年第4期42卷 642-660页

作者： Yi, Wenchao Gao, Liang Li, Xinyu Zhou, Yinzhi Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The differential evolution (DE) algorithm is a notably powerful evolutionary algorithm that has been applied in many areas. Therefore, the question of how to improve the algorithm's performance has attracted considerable attention from researchers. The mutation operator largely impacts the performance of the DE algorithm The control parameters also have a significant influence on the performance. However, it is not an easy task to set a suitable control parameter for DE. One good method is to considering the mutation operator and control parameters simultaneously. Thus, this paper proposes a new DE algorithm with a hybrid mutation operator and self-adapting control parameters. To enhance the searching ability of the DE algorithm, the proposed method categorizes the population into two parts to process different types of mutation operators and self-adapting control parameters embedded in the proposed algorithm framework. Two famous benchmark sets (including 46 functions) are used to evaluate the performance of the proposed algorithm and comparisons with various other DE variants previously reported in the literature have also been conducted. Experimental results and statistical analysis indicate that the proposed algorithm has good performance on these functions.

关键词： Hybrid mutation operator Self-adapting parameter control strategy differential evolution algorithm Global optimization problems

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Self-adaptive differential evolution algorithm with discrete mutation control parameters

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第3期42卷 1551-1572页

作者： Fan, Qinqin Yan, Xuefeng E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： evolutionary computation differential evolution algorithm Discrete mutation parameters Control parameter adaptation Mutation strategy adaptation

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Model Optimization for an Industrial Fluid Catalytic Cracking Riser-regenerator Unit by differential evolution algorithm

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PETROLEUM SCIENCE AND TECHNOLOGY 2015年第13-14期33卷 1380-1387页

作者： Long, J. Mao, M. S. Zhao, G. Y. E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

A differential evolution (DE) algorithm for determining 16 advanced turning model parameters of a 1.2 million industrial fluid catalytic cracking (FCC) unit modeling by HYSYS 8.4 is presented. Industrial data from a Chinese petroleum refinery were used to develop, train and check the model. Due to FCC complexity, the proposed model is capable of predicting the yield of products based on main operating conditions. The optimized FCC model is used for further optimized analysis of the FCC unit operating conditions based on the maximum of economic benefits. Prediction of the economic benefit of FCC unit increases 917 yuan/h at the optimized operating conditions.

关键词： fluid catalytic cracking optimization unit parameters differential evolution algorithm economic benefit

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Design of Three-Phase Induction Machine Using differential evolution algorithm

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IEEE LATIN AMERICA TRANSACTIONS 2015年第7期13卷 2202-2208页

作者： Malagoli, J. A. Camacho, J. R. Ferreira da Luz, M. V. Ferreira, J. H. I. Sobrinho, A. M. Univ Fed Uberlandia BR-38400 Uberlandia MG Brazil Univ Fed Santa Catarina Florianopolis SC Brazil Inst Fed Triangulo Min Ensino Basico Tecn & Tecnol Ituiutaba MG Brazil

In the present day, design of engineering systems appears as a line of research of great interest due the many applications that can be found in different areas of science and engineering. In this setting, design of induction motor, whose functions base are induce alternating currents in the rotor circuit, by the magnetic field rotating produced the stator coils, appears as an interesting theme research already which is directly related to manufacturing costs motors. In this context, this work aims the design of a three-phase induction motor using the differential evolution algorithm. For this purpose considered minimizing loss and cost on motor by determining the geometric variables vector characterizing the model mathematical presented. To solve these problems is used the MODE algorithm (Multiobjective Optimization differential evolution) and the outcome is compared to the NSGA II algorithm (Non-dominated Sorting Genetic algorithm II).

关键词： Design of Induction Motor differential evolution algorithm Optimization

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Modelling the influence of pH, fluoride, and caffeine on the corrosion resistance of TiMo alloys by artificial neural networks developed with differential evolution algorithm

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MATERIALS AND CORROSION-WERKSTOFFE UND KORROSION 2015年第9期66卷 982-994页

作者： Mareci, D. Dragoi, E. -N. Bolat, G. Chelariu, R. Gordin, D. Curteanu, S. Gheorghe Asachi Tech Univ Iasi Fac Chem Engn & Environm Protect Iasi 700050 Romania Gheorghe Asachi Tech Univ Iasi Fac Mat Sci & Engn Iasi 700050 Romania INSA Rennes F-35708 Rennes France

Titanium (Ti) and its alloys are widely used in dental applications due to the excellent corrosion resistance and mechanical properties. However, it has been reported that Ti is sensitive to fluor ions (F-) and lactic acid. Corrosion behaviour of the TiMo alloys, together with the currently used metallic biomaterial commercial pure titanium (Cp-Ti), was investigated considering the use of alloys for dental applications. All the samples were examined using electrochemical impedance spectroscopy (EIS) in acidic artificial saliva with NaF and/or caffeine, at 37 degrees C. Equivalent circuits were used for modeling EIS data, in order to characterize samples surface and better understanding the effect of Mo addition on Cp-Ti. The TiMo alloys appear to possess superior corrosion resistance than Cp-Ti in all electrochemical media. In addition, a modelling technique based on differential evolution and artificial neural network was applied. The scope of this procedure was to determine an efficient model of the process and to eliminate the need for new experiments based on the predictions provided by the developed models.

关键词： acidic artificial saliva artificial neural networks caffeine differential evolution algorithm TiMo alloys

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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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