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Contemporary Computing and Applications (IC3A), International Conference on

作者： Sunil Kumar Gouda Ashok Kumar Mehta Department of Computer Science and Engineering Chaibasa Engineering College Chaibasa India Department of Computer Applications National Institute of Technology Jamshedpur India

ISBN: (数字)9781728154329

ISBN: (纸本)9781728154336

To solve real-world global optimization problem differential evolution algorithm is used as one of the best nature influenced algorithm. The use of different effective mutation strategies and proper selection of effective control parameters directly affect the performance and convergent rate of differential evolution method. Although its performance is very good but suffers from population diversity and *** this paper, new self-adaptive mutation strategies with booster vector to improve global optimization of DE/rand/1/bin and DE/best/1/bin is proposed. Elite archive strategies with dynamic adjustment of control parameter with booster vector added to afford more bandwidth for electing an effective mutant solution. The proposed algorithm is compared with five DE and six non-DE algorithms by using a set of twenty benchmark functions on COCO (comparing Continuous Optimizers) framework. The experimental result verifies that proposed self-adaptive strategies outperformed the competitors.

关键词： Control parameter differential evolution self-adaptive optimization control parameter self-adaptive Global optimization evolutionary algorithm differential evolution algorithms algorithms

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Intelligent Garbage Can Decision-Making Model evolution Algorithm for optimization of structural topology of plane trusses

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APPLIED SOFT COMPUTING 2012年第9期12卷 2719-2727页

作者： Kuo, Hsin-Chuan Chiu, Jinn-Tong Lin, Ching-Hai De Lin Inst Technol Dept Mech Engn Taipei Taiwan Natl Taiwan Ocean Univ Dept Syst Engn & Naval Architecture Taipei Taiwan

The optimum design of structural topology of trusses is widely acknowledged as the most difficult and challenging problem in the area of structural optimization. Based on differential evolution algorithms and using the framework of a Garbage Can Decision-Making Model, we proposed an Intelligent Garbage Can Decision-Making Model evolution Algorithm (IGCMEA) to simulate the decision-making process in human social organizations. In a decision-making process, when faced with issues such as unclear goals and methods, employee turnover and so forth, representatives of all participating parties will communicate, argue, compromise and adapt with each other in order to find a solution to the problems. Group meetings are conducted to choose the best solution in a more objective, reasonable and efficient manner. By applying the differential evolution (DE) algorithm and IGCMEA to perform an optimization test on the 100-dimensional Schwefel Function, we showed that IGCMEA can achieve an efficient and satisfactory result. We also optimized the truss topology using IGCMEA and obtained a better result than when using the Genetic Algorithm as in the literature, thus illustrating the superior power of IGCMEA. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Optimum design differential evolution algorithms Garbage Can Decision-Making Model Topology optimization

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Application of Anaerobic Digestion Model No. 1 to describe the syntrophic acetate oxidation of poultry litter in thermophilic anaerobic digestion

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BIORESOURCE TECHNOLOGY 2014年 167卷 495-502页

作者： Rivera-Salvador, Victor Lopez-Cruz, Irineo L. Espinosa-Solares, Teodoro Aranda-Barradas, Juan S. Huber, David H. Sharma, Deepak Toledo, J. Ulises Univ Autonoma Chapingo Dept Ingn Agroind Chapingo 56230 Estado De Mexic Mexico Univ Autonoma Chapingo Chapingo 56230 Estado De Mexic Mexico West Virginia State Univ Gus R Douglass Inst Institute WV 25112 USA Inst Politecn Nacl Unidad Profes Interdisciplinaria Biotecnol Mexico City 07340 DF Mexico West Virginia State Univ Dept Biol Institute WV 25112 USA

A molecular analysis found that poultry litter anaerobic digestion was dominated by hydrogenotrophic methanogens which suggests that bacterial acetate oxidation is the primary pathway in the thermophilic digestion of poultry litter. IWA Anaerobic Digestion Model No. 1 (ADM1) was modified to include the bacterial acetate oxidation process in the thermophilic anaerobic digestion (TAD). Two methods for ADM1 parameter estimation were applied: manual calibration with non-linear least squares (MC-NLLS) and an automatic calibration using differential evolution algorithms (DEA). In terms of kinetic parameters for acetate oxidizing bacteria, estimation by MC-NLLS and DEA were, respectively, km 1.12 and 3.25 +/- 0.56 kg(COD) kg(COD)(1) d(1), K-S 0.20 and 0.29 +/- 0.018 kg(COD)m(3) and Yac-st 0.14 and 0.10 +/- 0.016 kg(COD) kg(COD)(1). Experimental and predicted volatile fatty acids and biogas composition were in good agreement. Values of BIAS, MSE or INDEX demonstrate that both methods (MC-NLLS and DEA) increased ADM1 accuracy. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： differential evolution algorithms Non-linear least squares Kinetic parameters Parameter estimation Anaerobic digestion model

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Calibrating a traffic flow model with parallel differential evolution

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Civil-Comp Proceedings 2017年 111卷

作者： Strofylas, G.A. Porfyri, K.N. Nikolos, I.K. Delis, A.I. Papageorgiou, M. School of Production Engineering and Management Technical University of Crete Chania Crete Greece

Given the importance of the credibility and validity required in macroscopic traffic flow models while performing real-word simulations, the necessity of employing an efficient, computationally fast and reliable constrained optimization scheme for model calibration appears to be mandatory to ensure that the traffic flow characteristics are accurately represented by such models. To this end, a parallel, metamodel-assisted differential evolution (DE) algorithm is employed for the calibration of the secondorder macroscopic gas-kinetic traffic flow (GKT) model using real traffic data from Attiki Odos freeway in Athens, Greece. The parallelization of the DE algorithm is performed using Message Passing Interface (MPI), while artificial neural networks (ANNs) are used as surrogate models. Numerical simulations are performed, which demonstrate that the DE algorithm can be effectively used for the search of the globally optimal model parameters in the GKT model; in fact the method appears to be promising for the calibration of other similar traffic models as well. © Civil-Comp Press, 2017.

关键词： Traffic control Constrained optimization evolutionary algorithms Kinetic theory of gases Message passing Neural networks Numerical methods differential evolution differential evolution algorithms Macroscopic traffic flow model Message passing interface Model calibration Surrogate model Traffic flow characteristics Traffic flow modeling

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Research on adaptive niche differential evolution algorithm for reactive power optimization

Research on adaptive niche differential evolution algorithm ...

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8th International Conference on Advances in Power System Control, Operation and Management (APSCOM 2009)

作者： Shu Xia Zifa Liu Jianhua Zhang Qi Yang School of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781617820564

Reactive power optimization which is the foundation for optimal control of voltage and reactive power is a very complex non-linear mixed integer programming problem. To deal with this optimization problem, an Adaptive Niche differential evolution algorithm (ANDE) is presented to avoid the premature phenomenon in DE. To maintain the population diversity, niche-sharing mechanism is adopted. To improve the rate of convergence, penalty function is used by niche-eliminating mechanism to deal with some low fitness individuals. To improve searching capability of algorithm, niching radius can also be adjusted adaptively on the basis of the relative distance between individuals which reflect the aggregation of population. Using the above method, the global searching ability of this algorithm could be improved. The proposed algorithm has been tested on IEEE-6, IEEE-30 and IEEE-118 bus systems and the calculation proves the effectiveness and practicability of the above algorithm.

关键词： Reactive power differential evolution algorithms niches algorithms evolutionary algorithm Optimal control Self tuning

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An Image Enhancement Optimization Method Based on differential evolution Algorithm and Cuckoo Search Through Serial Coupled Mode

An Image Enhancement Optimization Method Based on Differenti...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： Zhiwei Ye Ye Cao Aixin Zhang Can Jin Lie Ma Xiang Hu Jiwei Hu Hubei University of Technology Wuhan Jizhi Technology co. LTD Wuhan Fiberhome Technical Services Co. Ltd.

ISBN: (纸本)9781728140704

Image enhancement based on Beta function is a widely used method for it is able to fit multiple transformation curves, which is a significant step for image analysis. The key step for the method is to find the appropriate parameters to determine the grayscale transformation function. However, it needs a lot of time to seek applicable parameters when enumeration is used and random optimization algorithms often have failures within a limited time and are prone to fall into the local optimum. In order to solve the problems a serial coupled mode of stochastic optimization algorithms is investigated in the paper. According to the model, the differential evolution algorithm and cuckoo search algorithm are tried in image enhancement through serial coupling mode and compared with the traditional optimization algorithm. The experimental results reveals that the proposed approach is feasible and the performance is more balanced, which has a good performance on the image enhancement.

关键词： Image enhancement Hybrid algorithm differential evolution algorithm Cuckoo search algorithm image enhancement differential evolution algorithms Optimization algorithms SEARCHING ALGORITHM evolutionary algorithm Cuculidae Serial Publications BETA FUNCTIONS local optimum

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A Set-Based Discrete differential evolution Algorithm

A Set-Based Discrete Differential Evolution Algorithm

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Yu Liu Wei-Neng Chen Zhi-Hui Zhan Ying Lin Yue-Jiao Gong Jun Zhang Department of Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Sensor Network Ministry of Education Key Laboratory of Software Technology Education Department of Guangdong Province P.R. China

ISBN: (纸本)9781479906505

The TSP problem is considered as classical discrete optimization grouping problem, which is widely used in practice, but it is real a difficult NP problem. Simultaneously differential evolution (DE) algorithm has been proven to be a powerful optimization algorithm. Since the mutation process of DE contains a series of arithmetic operators operating on continuous space, few algorithms based on DE solve this problem nicely and the advantages of DE in continuous space cannot be used to solve TSP. To take full advantages of the strengths of DE, this paper proposes a set-based DE (S-DE) which completely follows the procedure of the original DE. We present a representation scheme to characterize the discrete problem space and by redefining its basic concept and all related operators in mutation, DE can operate directly on the original set space of the discrete optimization problems instead of performing a space transformation. In that way, the searching features of DE in continuous space is kept. In experiment, we test the performance of our proposed S-DE and the results show it is very promising.

关键词： component Discrete differential algorithm Sebased COP TSP MT CARD16 gene discrete element Optimization algorithms Total suspended particles evolutionary algorithm arithmetic operator NP problem CONTINUOUS SPACE differential evolution algorithms

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Feature Selection by differential evolution Algorithm - A Case Study in Personnel Identification

Feature Selection by Differential Evolution Algorithm - A Ca...

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IEEE Congress on evolutionary Computation

作者： Kingshuk Chakravarty Diptesh Das Aniruddha Sinha Amit Konar Innovation Lab Tata Consultancy Services Ltd. Electronics & Telecommunication Engineering Dept. Jadavpur University

ISBN: (纸本)9781479904532

Feature selection is an important area of research as it has a tremendous effect on the accuracy and performance of classification algorithms. In this paper we propose an objective function for feature selection, which combines the intra class feature variation and inter class feature distance using a Lagrangian multiplier. The inter class distance is measured using the sum of absolute difference of the ratio of mean and standard deviation for respective classes. The objective function is minimized using differential evolutionary (DE) Algorithm where the population vector is encoded using Binary Encoded Decimal to avoid the float number optimization problem. An automatic clustering of the possible values of the Lagrangian multiplier provides a detailed insight of the selected features during the proposed DE based optimization process. The classification accuracy of Support Vector Machine (SVM) is used to measure the performance of the selected features. The proposed algorithm outperforms the existing DE based approaches when tested on IRIS, Wine, Wisconsin Breast Cancer, Sonar and Ionosphere datasets. The same algorithm when applied on gait based people identification, using skeleton datapoints obtained from Microsoft Kinect sensor, exceeds the previously reported accuracies.

关键词： Feature extraction MT Lagrangian Objective function evolutionary algorithm differential evolution algorithms absolute difference Optimization Procedure Support Vector Network Multiplying circuits Case studies

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The Intelligent Search Method of Ambiguity Function Main Ridge Based on the Adaptive differential evolution Algorithm

The Intelligent Search Method of Ambiguity Function Main Rid...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yunwei Pu Lanyu Ma Wentai Hou Yuanpu Guo Computer Center Kunming Univ. of Science and Tech. Kunming China Faculty of Information Engineering and Automation Kunming Univ. of Science and Tech.

ISBN: (纸本)9781538653746

The ambiguity function is an effective tool to solve the problem of radar emitter signals sorting in complex systems. To improve the real-time and accuracy of the ambiguity function main ridge (AFMR), this paper proposes an intelligent method to search AFMR based on adaptive differential evolution algorithm. The signal is pre-identified by the instantaneous frequency (IF) features to reduce the AFMR search range and the complexity of the problem. In this way, the differential evolution algorithm parameters can be adaptively adjusted according to the pre-recognition results to accelerate the convergence of the algorithm. Then the statistical hypothesis testing theory is used to analyze and test the search results. The experiment results show that the proposed method reduces the time of searching AFMR to about 4% compared with the exhaustive method with higher accuracy and stability.

关键词： Radar emitter signal Adaptive differential evolution algorithm Ambiguity function main ridge Signal sorting and recognition differential evolution algorithms Antiferromagnetic resonance ambiguity function evolutionary algorithm ambiguity Intelligent retrieval instantaneous frequency ridges Crest

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A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps

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APPLIED SOFT COMPUTING 2005年第4期5卷 409-431页

作者： Papageorgiou, EI Groumpos, PP Univ Patras Artificial Intelligence Res Ctr UPAIRC Lab Automat & Robot Dept Elect & Comp Engn Rion 26500 Greece

A novel hybrid method based on evolutionary computation techniques is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods and algorithmic background. For this purpose, we investigate a coupling of differential evolution algorithm and unsupervised Hebbian learning algorithm, using both the global search capabilities of evolutionary strategies and the effectiveness of the nonlinear Hebbian learning rule. The use of differential evolution algorithm is related to the concept of evolution of a number of individuals from generation to generation and that of nonlinear Hebbian rule to the concept of adaptation to the environment by learning. The hybrid algorithm is introduced, presented and applied successfully in real-world problems, from chemical industry and medicine. Experimental results suggest that the hybrid strategy is capable to train FCM effectively leading the system to desired states and determining an appropriate weight matrix for each specific problem. (C) 2004 Elsevier B. V. All rights reserved.

关键词： Fuzzy Cognitive Maps learning algorithms nonlinear Hebbian rule evolutionary computation differential evolution algorithms evolutionary training

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