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Optimization and mechanism of acoustic absorption of Alberich coatings on a steel plate in water

APPLIED ACOUSTICS 2018年 140卷 183-187页

作者： Zhao, Dan Zhao, Honggang Yang, Haibin Wen, Jihong Natl Univ Def Technol Coll Intelligence Sci & Technol Lab Sci & Technol Integrated Logist Support Changsha 410073 Hunan Peoples R China

The acoustic absorption of Alberich coatings is generally a topic attracting persistent interests. The present paper optimizes acoustic absorption of two types of Alberich coatings on a steel plate immersed in water using a differential evolution algorithm combined with a finite element method. Both the coatings contain cylindrical cavities of mixed sizes. We compare the optimized absorption for different arrangement, i.e., the both coatings on different surfaces or only on the outer surface of the steel plate. The results show that the different coatings on both surfaces of the steel plate can achieve better sound absorption from 1.3 kHz to 10.0 kHz. The power dissipation density and the displacement pattern are used to analyze the broadband absorption mechanism induced by the acoustic resonance and the acoustic coupling between the optimized coatings. The results validate the idea that it can effectively enhance the low-frequency and bandwidth of the absorption by laying different coatings on both surfaces of the steel plate.

关键词： Underwater acoustic absorption Finite element method differential evolution algorithm Optimization

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Band selection for hyperspectral images based on particle swarm optimization and differential evolution algorithms with hybrid encoding

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2016年第3期16卷 629-640页

作者： Xu, Mengxi Sun, Quansen He, Zhenyu Shi, Jianqiang Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing Jiangsu Peoples R China Hohai Univ Coll Comp & Informat Engn Nanjing Jiangsu Peoples R China

evolutionary algorithms have been widely used in band selection for hyperspectral images. The particle swarm optimization (PSO) and the differential evolution (DE) algorithms are two common evolutionary techniques with efficient optimization capabilities. In order to fully utilize the advantages they provide, a band selection method is proposed based on the two algorithms with hybrid encoding. This method firstly uses hybrid encoding to make PSO and DE suitable for band selection. Secondly, the classification accuracy of an SVM classifier is used as the fitness function. Thirdly, we adopt the double population parallel iterative method to search for the optimal band combination. The experimental results on AVIRIS hyperspectral data show that the average classification accuracy of our proposed method is higher than the binary PSO algorithm, higher than the hybrid particle swarm algorithm, and higher than the hybrid coding differential evolution algorithm. These classification results demonstrate the effectiveness of the proposed method.

关键词： Hyperspectral images band selection particle swarm optimization differential evolution algorithm support vector machine

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Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system

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APPLIED ENERGY 2018年 231卷 645-659页

作者： Ahmadipour, Masoud Hizam, Hashim Othman, Mohammad Lutfi Radzi, Mohd Amran Mohd Murthy, Avinash Srikanta Univ Putra Malaysia Dept Elect & Elect Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia CAPER Serdang 43400 Malaysia Univ Putra Malaysia Ctr Electromagnet & Lightning Protect Res CELP Serdang 43400 Malaysia

In this paper, a new islanding detection technique is proposed for a three-phase grid connected photovoltaic inverter system using the multi-signal analysis method. The proposed strategy is divided into two steps: first step, all possible grid faults, switching transients and islanding events are simulated and the essential detection parameters are measured. By means of the Slantlet Transform theory, the energy, mean value, minimum, maximum, range, standard deviation and log energy entropy at any decomposition level of Slantlet Transform for parameter detection is computed and the best of them are selected as input data of second step. Second step, an advanced machine learning based on Ridgelet Probabilistic Neural Network is utilized to predict islanding and none islanding states. In order to train Ridgelet Probabilistic Neural Network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is proposed. The results depicting the effectiveness of the proposed method are explained and outcomes are drawn.

关键词： Islanding detection Inverter based distributed generation Slantlet Transform Ridgelet Probabilistic Neural Network differential evolution algorithm

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Multi-objective differential evolution with performance-metric-based self-adaptive mutation operator for chemical and qbiochemical dynamic optimization problems

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APPLIED SOFT COMPUTING 2017年 59卷 33-44页

作者： Fan, Qinqin Wang, Weili Yan, Xuefeng Shanghai Maritime Univ Logistics Res Ctr Shanghai 201306 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Each mutation operator of differential evolution (DE) algorithm is generally suitable for certain specific types of multi-objective optimization problems (MOPs) or particular stages of the evolution. To automatically select an appropriate mutation operator for solving MOPs in different phases of the evolution, a multi-objective differential evolution with performance-metric-based self-adaptive mutation operator (MODE-PMSMO) is proposed in this study. In MODE-PMSMO, a modified inverted generational distance (IGD) is utilized to evaluate the performance of each mutation operator and guide the evolution of mutation operators. The proposed MODE-PMSMO is then compared with seven multi-objective evolutionary algorithms (MOEAs) on five bi-objective and five tri-objective optimization problems. Generally, MODE-PMSMO exhibits the best average performance among all compared algorithms on ten MOPs. Additionally, MODE-PMSMO is employed to solve four typical multi-objective dynamic optimization problems in chemical and biochemical processes. Experimental results indicate that MODE-PMSMO is suitable for solving these actual problems and can provide a set of nondominated solutions for references of decision makers. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Chemical and biochemical processes differential evolution algorithm Dynamic optimization problem Multi-objective optimization

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Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2011年第7期24卷 1214-1226页

作者： Dragoi, Elena-Niculina Curteanu, Silvia Leon, Florin Galaction, Anca-Irina Cascaval, Dan Gheorghe Asachi Tech Univ Iasi Dept Chem Engn Iasi 700050 Romania Gheorghe Asachi Tech Univ Iasi Dept Comp Sci & Informat Technol Iasi 700050 Romania Grigore T Papa Univ Med & Pharm Iasi Dept Biotechnol Iasi 700454 Romania

The search capabilities of the differential evolution (DE) algorithm - a global optimization technique make it suitable for finding both the architecture and the best internal parameters of a neural network, usually determined by the training phase. In this paper, two variants of the DE algorithm (classical DE and self-adaptive mechanism) were used to obtain the best neural networks in two distinct cases: for prediction and classification problems. Oxygen mass transfer in stirred bioreactors is modeled with neural networks developed with the DE algorithm, based on the consideration that the oxygen constitutes one of the decisive factors of cultivated microorganism growth and can play an important role in the scale-up and economy of aerobic biosynthesis systems. The coefficient of mass transfer oxygen is related to the viscosity, superficial speed of air, specific power, and oxygen-vector volumetric fraction (being predicted as function of these parameters) using stacked neural networks. On the other hand, simple neural networks are designed with DE in order to classify the values of the mass transfer coefficient oxygen into different classes. Satisfactory results are obtained in both cases, proving that the neural network based modeling is an appropriate technique and the DE algorithm is able to lead to the near-optimal neural network topology. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Neural networks differential evolution algorithm Prediction Classification Bioreactors

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A fuzzy clustering iterative model using chaotic differential evolution algorithm for evaluating flood disaster

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 10060-10065页

作者： He, Yaoyao Zhou, Jianzhong Kou, Pangao Lu, Ning Zou, Qiang Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China

Flood disaster is a kind of frequent natural hazards. The objective of flood disaster evaluation is to establish hazard assessment model for managing flood and preventing disaster. Base on the chaotic optimization theory, this paper proposes a chaotic differential evolution algorithm to solve a fuzzy clustering iterative model for evaluating flood disaster. By using improved logistic chaotic map and penalty function, the objective function can be solved more perfectly. Two practical flood disaster cases have been taken into account so as to test the effect of novel hybrid method. Simulation results and comparisons show that the chaotic differential evolution algorithm is competitive and stable in performance with simple differential evolution and other optimization approaches presented in literatures. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Flood disaster evaluation evolutionary computation differential evolution algorithm Fuzzy clustering Chaotic map

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Optimal chiller loading by differential evolution algorithm for reducing energy consumption

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ENERGY AND BUILDINGS 2011年第2-3期43卷 599-604页

作者： Lee, Wen-Shing Chen, Yi-Ting Kao, Yucheng Natl Taipei Univ Technol Dept Energy & Refrigerating Air Conditioning Engn Taipei Taiwan Tatung Univ Dept Informat Management Taipei Taiwan

This study employs differential evolution algorithm to solve the optimal chiller loading problem for reducing energy consumption. To testify the performance of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with those of the Lagrangian method, genetic algorithm and particle swarm algorithm. The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions. Moreover, it outperforms the genetic algorithm in finding optimal solution and also overcomes the divergence problem caused by the Lagrangian method occurring at low demands. (C) 2010 Elsevier B.V. All rights reserved.

关键词： differential evolution algorithm Chiller loading Engineering optimization

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Application of an Adaptive differential evolution algorithm With Multiple Trial Vectors to Artificial Neural Network Training

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2011年第8期58卷 3160-3167页

作者： Slowik, Adam Koszalin Univ Technol Dept Elect & Comp Sci PL-75453 Koszalin Poland

In this paper, an application of an adaptive differential evolution (DE) algorithm with multiple trial vectors for training artificial neural networks (ANNs) is presented. The proposed method is DE-ANNT+, which is a DE-ANN Training (DE-ANNT) modified by adding a multiple trial vectors technique. DE-ANNT+ allows one to train an ANN of arbitrary architectures, and it offers a nondifferentiable neuron activation function. In contrast to a basic DE algorithm, DE-ANNT+ possesses two modifications. In DE-ANNT+, adaptive selection of control parameters and a multiple trial vectors technique are introduced. Adaptive selection means that the number of required parameters of the algorithm is decreased. The multiple trial vectors technique increases the probability of generating a better solution because a greater number of temporary solutions is generated around the existing solutions. The DE-ANNT+ algorithm, with these two modifications, is used for ANN training to classify the parity-p problem. The results from the obtained algorithm have been compared with results from the following algorithms: an evolutionary algorithm, a DE algorithm without multiple trial vectors, gradient training methods, such as error back-propagation, and the Levenberg-Marquardt method.

关键词： Artificial intelligence artificial neural network differential evolution algorithm multiple trial vectors training method

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Improved Fukunaga-Koontz Transform with Compositional Kernel Combination for Hyperspectral Target Detection

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2018年第10期46卷 1605-1615页

作者： Binol, Hamidullah Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

This article presents a novel supervised target detection approach on hyperspectral images based on Fukunaga-Koontz Transform (FKT) with compositional kernel combination. The Fukunaga-Koontz Transform is one of the most effective techniques for solving problems that involve two-pattern characteristics. To capture nonlinear properties of data, researchers have extended FKT to kernel FKT (KFKT) by means of kernel machines. However, the performance of KFKT depends on choosing convenient kernel functions and/or selection of the proper parameter(s). In this work, instead of selecting a single kernel for nonlinear version of FKT, we have applied a compositional kernel combination approach to capture the underlying local distributions of hyperspectral remote sensing data. Optimal parameter selection for each kernel function is achieved applying an evolutionary technique called differential evolution algorithm. The proposed new nonlinear target detection algorithm is tested for hyperspectral images. The experimental results verify that the proposed target detection algorithm has effective and promising performance compared to the conventional version for supervised target detection applications.

关键词： Compositional kernel combination differential evolution algorithm Fukunaga-Koontz transform Hyperspectral imagery Target detection

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A fast differential evolution algorithm using k-Nearest Neighbour predictor

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4254-4258页

作者： Liu, Yang Sun, Fan Heriot Watt Univ Sch Built Environm Edinburgh EH14 4AS Midlothian Scotland Univ E Anglia Norwich Business Sch Norwich NR4 7TJ Norfolk England

Genetic algorithms (GAS), particle swarm optimisation (PSO) and differential evolution (DE) have proven to be successful in engineering optimisation problems. The limitation of using these tools is their expensive computational requirement. The optimisation process usually needs to run the numerical model and evaluate the objective function thousands of times before converging to an acceptable solution. However, in real world applications, there is simply not enough time and resources to perform such a huge number of model runs. In this study, a computational framework, known as DE-kNN, is presented for solving computationally expensive optimisation problems. The concept of DE-kNN will be demonstrated via one novel approximate model using k-Nearest Neighbour (kNN) predictor. We describe the performance of DE and DE-kNN when applied to the optimisation of a test function. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well as provide considerable savings of the function calls compared to DE algorithm. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： differential evolution algorithm k-Nearest Neighbour Meta-model Optimisation

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