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An improved phase field method by using statistical learning theory-based optimization algorithm for simulation of martensitic transformation in NiTi alloy

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COMPUTATIONAL MATERIALS SCIENCE 2020年 172卷

作者： Wei, C. Ke, C. B. Liang, S. B. Cao, S. Ma, H. T. Zhang, X. P. South China Univ Technol Sch Mat Sci & Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510640 Guangdong Peoples R China Guangzhou Univ Earthquake Engn Res & Test Ctr Guangzhou Guangdong Peoples R China

An improved phase field method by using statistical learning theory based optimization algorithm is developed for solving the phase field equations through building simple relationships between the key phase field variables and the phase evolution driving force, and using statistical analysis of mass computed data during phase field simulation. Phase field simulation results of growth of R phase and the B2-R phase transformation in a Ni-rich Ni50.5Ti49.5 alloy by using the proposed statistical strategy algorithm are compared with that using the conventional numerical algorithm, which demonstrates that with coupling the statistical learning theory, i.e., by means of the optimization algorithm, the credible simulated microstructure is obtained while maintaining high accuracy, and meanwhile the computational time has been significantly reduced.

关键词： Martensitic transformation Phase field modeling optimization algorithm Statistical learning theory

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Virtual reconstruction of random moving image capturing points based on chaos embedded particle swarm optimization algorithm

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MICROPROCESSORS AND MICROSYSTEMS 2020年 75卷

作者： Qu, Chen Northeast Normal Univ Changchun 130024 Jilin Peoples R China

During the exercise process, such as jumping, and running, the angles of different joints of the athlete's body will produce different changes, resulting in obvious mismatches in different joint feature points. The chaotic matching error makes the calculation results of the basic shape features of the athletes in different parts have large deviations. In this paper, an improved algorithm of CEPS (Chaos embedded particle swarm) optimization algorithm interpolation is introduced, which can improve the interpolation precision and reduce the calculation time of interpolation. Through interpolation, the fast Fourier transform can be used to realize the fast reconstruction of the target 3d image based on biomechanical characteristics. The experimental and simulation results show that the three-dimensional image reconstruction algorithm based on the interpolation method can reconstruct the three-dimensional image of the target, achieve the detection of the target and have good resolution, and improve the authenticity of the human motion image sequence three-dimensional dynamic simulation. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Chaos embedded particle swarm optimization algorithm Chaos error control Image capturing points Virtual reconstruction Football athletes

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Characterization of polyvinyl alcohol hydrogels as tissue-engineered cartilage scaffolds using a coupled finite element-optimization algorithm

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JOURNAL OF BIOMECHANICS 2020年 99卷 109525-109525页

作者： Nazouri, Mohadeseh Seifzadeh, Alireza Masaeli, Elahe Islamic Azad Univ Najafabad Branch Dept Med Engn Najafabad Iran ACECR Royan Inst Biotechnol Cell Sci Res Ctr Dept Cellular Biotechnol Esfahan Iran Islamic Azad Univ Khomeinishahr Branch Dept Med Engn Khomeinishahr Iran

Mechanical strength along with high biocompatibility and water absorbing are among main characteristics of a desirable scaffold for cartilage tissue engineering. Having these properties, polyvinyl alcohol (PVA) can be a good option for constructing cartilage tissue engineering scaffolds. In this study, PVA hydrogel was produced by freeze-thaw crosslinking method, and its mechanical properties such as viscoelastic and hyperplastic behavior, which cannot be obtained analytically, was investigated with a coupled finite element (FE)-optimization algorithm and stress relaxation experimental data. To obtain isotropic hyper-viscoelastic constitutive parameters of PVA scaffolds, the Mooney-Rivlin and Neo-Hooke strain energy functions, in which shear and bulk moduli varies with time, were applied. Results showed that predicted mechanical responses of scaffolds by the Mooney-Rivlin model better fitted stress-relaxation experiments than those obtained by Neo-Hooke one. Also, the properties obtained from the finite element model, such as the bulk and the shear moduli, showed that, after successful in vitro and in vivo experiments, PVA hydrogel may be introduced as a cartilage substitute for future tissue engineering therapies. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cartilage Polyvinyl alcohol optimization algorithm Finite Element

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TLBO optimization algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

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TRAITEMENT DU SIGNAL 2020年第4期37卷 541-553页

作者： Ouali, Mohammed Assam Ghanai, Mouna Chafaa, Kheireddine Univ Msila Fac Technol Elect Dept LAAS Lab Msila 28000 Algeria Caen Univ GREYC Lab F-14000 Caen France Univ Batna 2 Elect Dept LAAAS Lab Fac Technol Batna 5000 Algeria

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals;these included real and artificial noise. For comparison purposes, both model and non-model-based methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

关键词： ECG signal ECG denoising type-2 fuzzy logic optimization algorithm TLBO

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Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems

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ADVANCES IN ENGINEERING SOFTWARE 2020年 146卷

作者： Das, Bikash Mukherjee, V. Das, Debapriya Govt Coll Engn & Textile Technol Dept Elect Engn Berhampur W Bengal India Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad Bihar India Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India

In this article, a new metaheuristic optimization algorithm (named as, student psychology based optimization (SPBO)) is proposed. The proposed SPBO algorithm is based on the psychology of the students who are trying to give more effort to improve their performance in the examination up to the level for becoming the best student in the class. Performance of the proposed SPBO is analyzed while applying the algorithm to solve thirteen 50 dimensional benchmark functions as well as fifteen CEC 2015 benchmark problems. Results of the SPBO is compared to the performance of ten other state-of-the-art optimization algorithms such as particle swarm optimization, teaching learning based optimization, cuckoo search algorithm, symbiotic organism search, covariant matrix adaptation with evolution strategy, success-history based adaptive differential evolution, grey wolf optimization, butterfly optimization algorithm, poor and rich optimization algorithm, and barnacles mating optimizer. For fair analysis, performances of all these algorithms are analyzed based on the optimum results obtained as well as based on convergence mobility of the objective function. Pairwise and multiple comparisons are performed to analyze the statistical performance of the proposed method. From this study, it may be established that the proposed SPBO works very well in all the studied test cases and it is able to obtain an optimum solution with faster convergence mobility.

关键词： Benchmark function CEC 2015 Global optimum solution optimization algorithm Student psychology based optimization (SPBO)

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An Agglomerative Greedy Brain Storm optimization algorithm for Solving the TSP

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IEEE ACCESS 2020年 8卷 201606-201621页

作者： Wu, Changyou Fu, Xisong Shandong Inst Business & Technol Sch Management Sci & Engn Yantai 264005 Peoples R China

The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this article, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this article consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.

关键词： Brain storm optimization algorithm traveling salesman problem hierarchical clustering optimization algorithm combinatorial optimization

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Joint image compression and encryption using a novel column-wise scanning and optimization algorithm

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Procedia Computer Science 2020年 167卷 244-253页

作者： Pratibha Chaudhary Ritu Gupta Abhilasha Singh Pramathesh Majumder Ayushi Pandey

In this research, a joint image compression and encryption scheme is presented which works for different image dimensions 256X256, 512X512 and 1024X1024 and different image sizes. The proposed scheme is efficient as compression and encryption is done simultaneously which makes computations faster and secure at the same time. The compression scheme deployed is like JPEG compression but uses column-wise scanning and optimization instead of zigzag scanning. In entropy phase coding, Huffman coding of JPEG compression is replaced with Arithmetic coding. Image encrypted using XOR encryption is unbreakable, as used earlier in one-time pad. Compressed data is XORed with randomly generated number. The proposed scheme is implemented for grayscale images using MATLAB software version 2016a. The experimental results showed that the scheme provides good compression ratio, secures against attacks and takes less computation time. This can be further extended for RGB images of different dimension 256X256, 512X512 and 1024X1024 and different image sizes.

关键词： Column-wise scanning optimization algorithm DCT (Discrete cosine transform) Joint Compression Encryption JPEG Compression Quantization XOR Encryption

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Root Based optimization algorithm for Task-Oriented Structural Design of a Multi-Axial Road Test Rig

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IEEE ACCESS 2020年 8卷 168061-168078页

作者： Wang, Xiao Cong, Dacheng Yang, Zhidong Han, Junwei Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

A novel root based optimization algorithm (ROA), which mimics the proliferation of plant roots, is proposed to evaluate the performances of a multi-axial road test rig over a prescribed workspace. Plant roots have developed complex behavior patterns to search for nutrients in the soil and even exhibit swarm intelligence. The growing roots can adapt their actions such as elongation, branching, and tropistic movement according to the environment. During the process of foraging for water and nutrients, the adaptive growth behavior of roots can be simulated to tackle the optimization problem. The efficiency of the proposed ROA has been validated by comparing it with well-known intelligent algorithms using classical and IEEE CEC 2019 benchmark functions. Computational results indicate that ROA outperforms other algorithms to search a global optimum on several benchmarks. Furthermore, the results are tested with nonparametric statistical methods, e.g., Wilcoxon rank-sum test. Besides, ROA is applied to the optimal dimensioning synthesis of the multi-axial road test rig (MRTR) based on the orthogonal 6-RSS parallel manipulator to promote the device's performance. The inverse kinematics of the multi-axial road test rig based on the asymmetrical orthogonal 6-RSS parallel mechanism is analyzed in detail. Applying the achieved dimensionless Jacobian, the performance criteria involving the global isotropy index, the force/velocity transmissibility index, the decoupling index are established. By simultaneously taking account the proposed criteria of performance, structural optimization is carried out resorting to ROA. Finally, a set of optimized dimensional parameters is obtained. The practical application further illustrates that ROA has the potential to deal with complex optimization problems.

关键词： optimization Roads Particle swarm optimization Indexes Benchmark testing Kinematics Space exploration Root adaptive growth optimization algorithm benchmark test orthogonal 6-RSS parallel manipulator task-oriented performance criteria optimal structural design

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Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks optimization algorithm

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IEEE ACCESS 2020年 8卷 133076-133085页

作者： Golilarz, Noorbakhsh Amiri Mirmozaffari, Mirpouya Gashteroodkhani, Tayyebeh Asgari Ali, Liaqat Dolatsara, Hamidreza Ahady Boskabadi, Azam Yazdi, Mohammad Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Univ Guilan Dept Elect Engn Rasht *** Iran Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Clark Univ Sch Management Worcester MA 01610 USA Washington State Univ Carson Coll Business Dept Finance & Management Sci Pullman WA 99163 USA Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

In this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks optimization algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain. This optimization algorithm is the improved version of the previous HHO algorithm which consists of chaos, multi-population, and differential evolution strategies. In this study, we applied several optimization algorithms in the optimization procedure and we compared the de-noising results with CMDHHO based noise suppression as well as with the Thresholding Neural Network (TNN) approaches. It is observed that applying the CMDHHO algorithm provides us with better qualitative and quantitative results comparing with other optimized and TNN based noise removal techniques. In addition to the quality and quantity improvement, this method is computationally efficient and improves the processing time. Based on the experimental analysis, optimized based noise suppression performs better than TNN based image de-noising. Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate and measure the performance of different de-noising methods. Experimental results indicate the superiority of the proposed CMDHHO based satellite image de-noising over other available approaches in the literature.

关键词： optimization Image denoising Wavelet transforms Signal processing algorithms Noise reduction Wavelet domain Thresholding (Imaging) CMDHHO optimization algorithm satellite image de-noising TNN wavelet domain

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An Improved Animal Migration optimization algorithm Based on Interactive Learning Behavior for High Dimensional optimization Problem

An Improved Animal Migration Optimization Algorithm Based on...

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International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Lai, Zhaolin Feng, Xiang Yu, Huiqun East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728104669

Animal migration optimization(AMO) algorithm inspired by the behavior of animal migration is proposed recently. AMO shows good performance on the benchmark functions whose dimensionality is no more than 30. However, the performance of AMO is degraded rapidly when the dimensionality is larger than 30. In order to overcome this shortcoming, an improved animal migration algorithm (IAMO) based on interactive learning behavior is proposed in this paper. First, we introduce an interactive learning behavior that individuals will learn from each other by exchanging information. During the search process, the search step is dynamically adjusted. In this case, the intelligence of IAMO is higher than AMO. Second, a refined search method is used to search around the current solutions, and this method can enhance the search ability of the algorithm. Third, a birth-and-death mechanism is designed to avoid local optimum. The effectiveness of IAMO is verified on 100 dimensional benchmark functions, and the empirical results show that the performance of IAMO is promising.

关键词： optimization algorithm migration interactive learning birth-and-death mechanism high dimension

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