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International Conference on Advances in Mechanical, Industrial, Automation and Management Systems (AMIAMS)

作者： Dutta, Pooja Datta, Dilip Tezpur Univ Dept Mech Engn Napaam 784028 Tezpur India

ISBN: (纸本)9781509056743

The bi-level problem is an embedded decentralized optimization process at two levels arisen in hierarchy-based organizations. The activities of the lower-level are governed by the upper-level, while the performance of the latter is dependent upon that of the former. Though consists of two decentralized levels, their embedding nature makes the solution procedure of the bi-level problem complicated. In the present work, a multi-objective differential evolution algorithm is employed to solve this problem as a plain multi-objective optimization problem by combining its two levels. The proposed procedure is found very potential in identifying promising trade-off optimal solutions for a number of benchmark problems.

关键词： Bi-level Multi-Objective hierarchy-based Benchmark testing plains Decentralized Optimization Procedure optimal solution embedding differential evolution algorithms

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differential evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

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APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2017年第1期2017卷 7974218.1-7974218.8页

作者： Mohamed, Ali Wagdy Almazyad, Abdulaziz S. Al Yamamah Univ Coll Comp & Informat Syst POB 45180 Riyadh 11512 Saudi Arabia Cairo Univ Inst Stat Studies & Res Dept Operat Res Giza 12613 Egypt King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

Copyright (C) 2017 Ali Wagdy Mohamed and Abdulaziz S. Almazyad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper presents differential evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

关键词： Novel Mutation Global optimization change in value Scale formation differential evolution algorithms evolutionary algorithm

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An Ensemble of differential evolution algorithms for Constrained Function Optimization

An Ensemble of Differential Evolution Algorithms for Constra...

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2010 IEEE World Congress on Computational Intelligence

作者： Tasgetiren, M. Fatih Suganthan, P. Nagaratnam Pan, Quan-Ke Mallipeddi, Rammohan Sarman, Sedat Yasar Univ Dept Ind Engn Izmir Turkey Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China

ISBN: (纸本)9781424481262

This paper presents an ensemble of differential evolution algorithms employing the variable parameter search and two distinct mutation strategies in the ensemble to solve real-parameter constrained optimization problems. It is well known that the performance of DE is sensitive to the choice of mutation strategies and associated control parameters. For these reasons, the ensemble is achieved in such a way that each individual is assigned to one of the two distinct mutation strategies or a variable parameter search (VPS). The algorithm was tested using benchmark instances in Congress on evolutionary Computation 2010. For these benchmark problems, the problem definition file, codes and evaluation criteria are available in http://***/home/EPNSugan. Since the optimal or best known solutions are not available in the literature, the detailed computational results required in line with the special session format are provided for the competition.

关键词： benchmark instances benchmark testing codes codes constrained function optimization differential evolution algorithms evolutionary computation evolutionary computation 2010 mutation strategies problem definition file search problems variable parameter search

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differential evolution ALGORITHM-BASED RANGE IMAGE REGISTRATION WITH SCALING PARAMETERS

DIFFERENTIAL EVOLUTION ALGORITHM-BASED RANGE IMAGE REGISTRAT...

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IEEE International Conference on Image Processing

作者： Taifeng Li Liang Gao Quanke Pan Peigen Li The State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science & Technology

ISBN: (纸本)9781467399623

Range image registration is used to align two or more three dimensional (3D) point sets into a common coordinate system. In the matching process, however, the influence of the resolution of 3D scans is ignored generally. In this paper, an enhanced differential evolution (DE) algorithm is proposed to align two different scaling 3D point sets. Specifically, Generalized Procrustes Analysis (GPA) method is employed to accelerate the DE in population initialization. In addition, a novel mutation technique is introduced to improve the accuracy for registration. The proposed method can evaluate all of the transformation parameters synchronously and it is effective for both isotropic and anisotropic scaling registration problem. Experiment results reveal that the proposed algorithm is much superior to other methods in terms of accuracy and robustness for scaling registration problem.

关键词： Range image Registration Transformation parameters differential evolution algorithm range image differential evolution algorithms Registration image registration Scale formation scale parameter Coordinate system

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Pilot Symbol Assisted Weighted Data Fusion Scheme for Uplink Base-Station Cooperation System

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2015年第2期9卷 528-544页

作者： Zhang, Zhe Yang, Jing Zhang, Jiankang Mu, Xiaomin Zhengzhou Univ Sch Informat Engn Zhengzhou 450052 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450000 Peoples R China

Base Station Cooperation (BSC) has been a promising technique for combating the Inter-Cell Interference (ICI) by exchanging information through a high-speed optical fiber back-haul to increase the diversity gain. In this paper, we propose a novel pilot symbol assisted data fusion scheme for distributed Uplink BSC (UBSC) based on differential evolution (DE) algorithm. Furthermore, the proposed scheme exploits the pre-defined pilot symbols as the sample of transmitted symbols to constitute a sub-optimal Weight Calculation (WC) model. To circumvent the non-linear programming problem of the proposed sub-optimal model, DE algorithm is employed for searching the proper fusion weights. Compared with the existing equal weights based soft combining scheme, the proposed scheme can adaptively adjust the fusion weights according to the accuracy of cooperative information, which remains the relatively low computational complexity and back-haul traffic. Performance analysis and simulation results show that, the proposed scheme can significantly improve the system performance with the pilot settings of the existing standards.

关键词： Uplink base station cooperation inter-cell interference weighted data fusion pilot symbols differential evolution algorithms

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Parameter identification of an SOFC model with an efficient, adaptive differential evolution algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2014年第10期39卷 5083-5096页

作者： Gong, Wenyin Cai, Zhihua Yang, Jie Li, Xi Jian, Li China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Sch Mech & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China

An efficient, adaptive differential evolution (DE) algorithm is proposed in which DE parameter adaptation is implemented. A ranking-based vector selection and crossover rate repairing technique are also presented. The method is referred to as DADE (Improved Jingqiao Adaptive DE). To verify the performance of DADE, the parameters of a simple SOFC electrochemical model that is used to control the output performance of an SOFC stack are identified and optimized. The SOFC electrochemical model is built to provide the simulated data. The results indicate that the proposed method is able to efficiently identify and optimize model parameters while showing good agreement with both simulated and experimental data. Additionally, when compared to other DE variants and other evolutionary algorithms, DADE obtained better results in terms of the quality of the final solutions, robustness, and convergence speed. Copyright (c) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： differential evolution algorithms Electrochemical model Parameter identification Solid oxide fuel cell (SOFC)

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An Optimal Radial Layout for High Dimensional Data Class Visualization

An Optimal Radial Layout for High Dimensional Data Class Vis...

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International Conference on Advanced Technologies for communications (ATC)

作者： Tran Van Long Vu Thi Ngan Univ Transport & Commun Fac Basic Sci Hanoi Vietnam Hanoi Coll Commerce & Tourism Fac Basic Sci Ha Noi City Vietnam

ISBN: (纸本)9781467383745

Multivariate data visualization is an interesting research field with many applications in ubiquitous fields of sciences. Radial visualization is one of the most common information visualization techniques for visualizing multivariate data. Unfortunately, Radial visualization display different information about structures of multivariate data on the different positions of dimensional anchors on the unit circle. In this paper, we propose a method that improve the Radviz layout for class visualization of high-dimensional data. We apply the differential evolution algorithm to find the optimal dimensional anchors of the RadViz such that maximum the quality of Radial visualization for classifier data. We use the k nearest neighbors classifier for quality measurement. Our method provides an improvement visualizing class structures of high-dimensional data sets on the RadViz. We demonstrate the efficiency of our method for some data sets.

关键词： dimensional data Visualization Unit circle k nearest neighbor Radial Data visualisation Quality management differential evolution algorithms information visualization layouts Boat anchors Multivariate

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differential evolution Algorithm Based Pilot Aided Weighted Data Fusion Scheme for Uplink Base-Station Cooperation System 6

Differential Evolution Algorithm Based Pilot Aided Weighted ...

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6th International Conference on Wireless Communications and Signal Processing (WCSP)

ISBN: (纸本)9781479973392

Base Station Cooperation (BSC), has been a promising technique for combating the Inter-Cell Interference (ICI) by exchanging information through a high-speed optical fiber backhaul to increase the diversity gain. In this paper, we propose a novel pilot symbol assisted data fusion scheme for Uplink BSC (UBSC). Furthermore, the proposed scheme exploits the pre-defined pilot symbols as the sample of transmitted symbols to constitute a sub-optimal Weight Calculation (WC) model. To circumvent the non-linear programming problem of the proposed sub-optimal model, differential evolution (DE) algorithm is employed for searching the proper fusion weights. Compared with the existing equal weights based soft combining scheme, the proposed scheme can adaptively adjust the fusion weights according to the accuracy of cooperative information, which remains the relatively low computational complexity and backhaul traffic. Performance analysis and simulation results show that, the proposed scheme can significantly improve the system performance with the pilot settings of the existing standards.

关键词： Bit error rate Computational modeling Data integration Data models Optical wavelength conversion Uplink Vectors up link Optical wavelength conversion data integration data models Bit error rate Computational modeling Pilots differential evolution algorithms Data fusion Weighted data fusion Diversity methods

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Complex Network Analysis of differential evolution Algorithm applied to Flowshop with No-Wait Problem

Complex Network Analysis of Differential Evolution Algorithm...

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2014 IEEE Symposium on differential evolution (SDE)

作者： Davendra, Donald Zelinka, Ivan Metlicka, Magdalena Senkerik, Roman Pluhacek, Michal VSB Tech Univ Ostrava Dept Comp Sci 17 Listopadu 15 Ostrava 70833 Czech Republic Tomas Bata Univ Zlin Fac Appl Informat Dept Artificial Intelligence Zlin Czech Republic

ISBN: (纸本)9781479944613

This paper analyses the attributes of population dynamics of differential evolution algorithm using Complex Network Analysis tools. The population is visualised as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, whereas cliques outlines the depth of interconnection and subgraphs within the population. The community graph plot gives an overview of the hierarchical grouping of the individuals in the population. These attributes give a clear description of the population during evaluation and can be utilised for adaptive population and parameter control.

关键词： evolutionary computation flow shop scheduling graph theory network theory (graphs) FSSNW adjacency graph community graph plot complex network analysis differential evolution algorithm flow shop scheduling with no-wait problem Algorithm design and analysis Complex networks Educational institutions Sociology Statistics Vectors Visualization differential evolution algorithms complex networks evolutionary algorithm Algorithm design and analysis Network analysis Educational institutions Sociology Graph theory Visualization

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A Population Adaptation Mechanism for differential evolution Algorithm

A Population Adaptation Mechanism for Differential Evolution...

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IEEE Symposium Series on Computational Intelligence

作者： Johanna Aalto Jouni Lampinen Dept. of Comput. Sci. Univ. of Vaasa Vaasa Finland

ISBN: (纸本)9781479975617

In the original differential evolution algorithm three different control parameter values must be pre-specified by a user. These parameters are the population size, the crossover constant and the mutation scale factor. Control parameters affect strongly the performance and reliability of the algorithm. However, choosing good parameters can be very difficult for a user. In this paper a new adaptive differential evolution algorithm called Cumu-DE is proposed. The aim of the algorithm is to be a user friendly and reliable algorithm with moderate convergence speed. In the proposed algorithm, the so called effective population size is adapted automatically using mechanism based on probability mass function. The actual population size is kept fixed. Even though we talk about two different population sizes, we have only one population. The effective population size describes the effective part of the actual population. The more we get successful trials, the smaller the effective population is and vice versa. The algorithm was initially evaluated by using the set of 25 benchmark functions provided by CEC2005 special session on real-parameter optimization. It was compared with the results of standard DE/rand/1/bin. The proposed algorithm Cumu-DE proved to be significantly faster due to its average of FES in four cases and significantly slower in six cases. Additionally, Cumu-DE was significantly more reliable in six cases and significantly less reliable in none. These results are demonstrating the potential of the proposed adaptation approach.

关键词： differential evolution algorithms population size evolutionary algorithm control parameter effective population size Parameter optimization FES gene algorithms

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