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EAO: Enhanced aquila optimizer for solving optimization problem

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第2期46卷 4361-4380页

作者： Guo, Hairu Wang, Jin'ge Liu, Yongli Zhang, Yudong Henan Polytech Univ Dept Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia

The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm's convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO's viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO's robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design.

关键词： Aquila optimization algorithm optimization function kapur entropy threshold optimization pressure vessel design

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Research on rolling process of bow parts with variable lateral roll spacing based on optimal bending angle distribution function

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第1-2期114卷 407-422页

作者： Su, Chunjian Li, Zhen Yang, Hongxi Lou, Shumei Lv, Yuting Zhang, Guosong Wang, Rui Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Peoples R China

For roll-bending formation in bow-shaped sheet metal parts, the distance of the traditional roll-forming transverse unit is constant, which often causes corner wrinkles, corner cracks, tearing, springback, and peak longitudinal strain defects throughout the formation process. To solve these shortcomings, this paper proposes an optimized bending angle distribution function to synergize the variable lateral roll distance technique. By parameterizing the abstract function according to the function boundary trajectory, the bending angle of each roll pass is measured;furthermore, the optimal function curve can be obtained through finite element analysis. Then, the roll-bending simulation is conducted, and the forming angle of the sheet metal parts is redistributed to determine the most effective reduction of peak longitudinal strain and transverse roll spacing. The bending angle distribution function aids the variable transverse roll distance technology to execute roll-bending experiments on 3004Al arch parts, ultimately obtaining the optimal roll-bending angle and transverse roll distance. The results of the roll forming simulation, based on the optimized bending angle distribution function and variable transverse roll distance technology, corroborate the experimental results, and good roll forming efficiency and quality are achieved.

关键词： Bow-shaped sheet metal parts optimization function Roll forming Variable transverse roll spacing

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Single-channel speech enhancement based on joint constrained dictionary learning

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2021年第1期2021卷 1-14页

作者： Sun, Linhui Bu, Yunyi Li, Pingan Wu, Zihao Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing Peoples R China

To improve the performance of speech enhancement in a complex noise environment, a joint constrained dictionary learning method for single-channel speech enhancement is proposed, which solves the "cross projection" problem of signals in the joint dictionary. In the method, the new optimization function not only constrains the sparse representation of the noisy signal in the joint dictionary, and controls the projection error of the speech signal and noise signal on the corresponding sub-dictionary, but also minimizes the cross projection error and the correlation between the sub-dictionaries. In addition, the adjustment factors are introduced to balance the weight of constraint terms to obtain the joint dictionary more discriminatively. When the method is applied to the single-channel speech enhancement, speech components of the noisy signal can be more projected onto the clean speech sub-dictionary of the joint dictionary without being affected by the noise sub-dictionary, which makes the quality and intelligibility of the enhanced speech higher. The experimental results verify that our algorithm has better performance than the speech enhancement algorithm based on discriminative dictionary learning under white noise and colored noise environments in time domain waveform, spectrogram, global signal-to-noise ratio, subjective evaluation of speech quality, and logarithmic spectrum distance.

关键词： Single-channel speech enhancement Joint constraint Sparse representation Dictionary learning optimization function

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Exploring instance correlations with local discriminant model for multi-label feature selection

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APPLIED INTELLIGENCE 2022年第7期52卷 8302-8320页

作者： Fan, Yuling Liu, Jinghua Wu, Shunxiang Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 Peoples R China

Multi-Label Learning (MLL) is a classification task in which each instance may be associated with two or more class labels simultaneously. With the arrival of information explosion era, MLL is confronted with the curse of high-dimensional data. Feature selection is an effective technique that has been used to reduce dimensionality. Previous Multi-Label Feature Selection (MLFS) approaches only lay emphasis on the fitting relations between the feature space and the corresponding label space while neglecting the instance correlations. To remedy the deficiency, we present a novel MLFS method by Exploring Instance Correlations with Local Discriminant model (EICLD). More specifically, we first construct a local set for each instance, and use a local discriminant model for each local set to explore instance correlations. Then, we further integrate the local models of all instances to guarantee the global instance correlations. Finally, l(2,1)-norm is introduced into loss function and the regularization of feature weight matrix respectively to facilitate feature selection process. An optimization algorithm is designed for handling the proposed objective function. The paper is compared with seven representative approaches on twelve data sets. The results validate the superiority of the proposed approach.

关键词： Multi-label learning Feature selection Local discriminant model optimization function

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Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization

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RENEWABLE ENERGY 2020年 153卷 375-391页

作者： Zhou, Yuekuan Zheng, Siqian Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg Serv Engn Hong Kong Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Hunan Univ Coll Civil Engn Minist Educ Key Lab Bldg Safety & Energy Efficiency Changsha 410082 Hunan Peoples R China

Integrating advanced materials in building glazing systems is critical for promoting net-zero energy buildings. In this research, both experimental and numerical studies were conducted on an aerogel glazing system. In order to provide climate adaptive designs on the aerogel glazing system with optimal geometric and operating parameters, a generic optimization methodology was developed by flexibly integrating supervised machine learning and advanced teaching-learning-based optimization algorithm. The proposed optimization methodology was thereafter used for optimal system designs in different climate regions. Results indicate that the proposed surrogate model can intelligently and accurately learn and update the optimization function with straightforward mathematical associations between multi-variables and objectives. In addition, within optimal cases, total heat gain and heat flux are dominated by the extinction coefficient in southern cities, whereas the total heat gain is dominated by the thermal conductivity in the northern city, LanZhou. By adopting the proposed technique in this study, compared to optimal results following the Taguchi standard orthogonal array, the total heat gain can be reduced by 62.5% to 36.27 kWh/m(2) in LanZhou, and by 5.9% to 267.18 kWh/m(2) in GuangZhou, respectively. This study formulates a general methodology for climate adaptive optimal designs on aerogel glazing systems in different climatic regions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Aerogel glazing system Climatic regions Machine learning optimization function Teaching-learning-based optimization

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Optimized bending angle distribution function of contour plate roll forming

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期97卷 1787-1799页

作者： Su, Chun-Jian Yang, Long-Yun Lou, Shu-Mei Cao, Gao-Hua Yuan, Feng-Ru Wang, Qing Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Sch Mat Sci & Engn Qingdao 266590 Peoples R China

Regarding the roll forming of contour plates, traditional roll forming techniques often result in peak longitudinal strain during the forming process, which causes defects such as longitudinal bowing and springback. The bending angle, sheet thickness, and the number of forming passes are all important process parameters that cause the aforementioned problems. This study proposes quantifying the projection track regarding the edge of the profile section in the horizontal plane that follows a cubic curve and uses the curve function to reasonably distribute the bending angle to study the maximum forming strain and the average strain for all passes of the sheet between the forming passes. Simultaneously, the influence law of the sheet thickness and number of forming passes was studied. Based on theoretical simulation and experimental verification, the optimal bending angle distribution function is A1: y = x (3) + x (2) + x. When the thickness of the sheet is 1.5 mm, both the maximum forming strain between the passes during roll forming and the average strain of all passes are the smallest, which are 3.91 and 0.609%, respectively. Moreover, with the increase in the number of passes, the longitudinal bowing and springback decrease to varying degrees.

关键词： Bending angle optimization function Roll forming Peak longitudinal strain Longitudinal bow

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Experimental Investigations and Multi-response optimization of Silicon Dioxide (Quartz) Machining in Magnetic Field Assisted TW-ECSM Process

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SILICON 2017年第5期9卷 663-673页

作者： Rattan, Nav Mulik, R. S. Indian Inst Technol Roorkee Dept Mech & Indutrial Engn Roorkee 247667 Uttar Pradesh India

Travelling wire electrochemical spark machining (TW-ECSM) has the potential for the machining of advanced non-conducting materials like glass, quartz, silicon nitride, various composites and ceramics. In the present work, experimental investigations have been conducted on the magnetic field assisted travelling wire electrochemical spark machining (TW-ECSM) process. In this technique, a magnetic field has been introduced in the TW-ECSM process in order to improve the machining performance in terms of improved material removal rate (MRR) and reduced surface roughness (Ra) of non-conducting materials. Material removal rate (MRR) and surface roughness (Ra) were selected as the responses during the experimentation. The experimental results revealed that the magnetic field assisted approach gives reduced values of Ra and increased MRR when compared to the TW-ECSM process. The minimum and maximum values of surface roughness and MRR were found to be 7.42 A mu m and 0.19 mg/min with magnetic field assisted TW-WCSM process respectively. The optimization function has been developed to minimize Ra and maximize MRR for obtaining the optimum process parameters.

关键词： Quartz Magnetic field TW-ECSM MATLAB optimization function

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Predicting Metabolic Fluxes Using Gene Expression Differences As Constraints

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011年第1期8卷 206-216页

作者： van Berlo, Rogier J. P. de Ridder, Dick Daran, Jean-Marc Daran-Lapujade, Pascale A. S. Teusink, Bas Reinders, Marcel J. T. Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Bioinformat Lab NL-2628 CD Delft Netherlands Kluyver Ctr Genom Ind Fermentat NL-2628 BC Delft Netherlands Delft Univ Technol Ind Microbiol Grp Dept Biotechnol NL-2628 CD Delft Netherlands Vrije Univ Amsterdam Fac Earth & Life Sci Syst Bioinformat IBIVU NL-1081 HV Amsterdam Netherlands

A standard approach to estimate intracellular fluxes on a genome-wide scale is flux-balance analysis (FBA), which optimizes an objective function subject to constraints on (relations between) fluxes. The performance of FBA models heavily depends on the relevance of the formulated objective function and the completeness of the defined constraints. Previous studies indicated that FBA predictions can be improved by adding regulatory on/off constraints. These constraints were imposed based on either absolute [21], [3] or relative [20] gene expression values. We provide a new algorithm that directly uses regulatory up/down constraints based on gene expression data in FBA optimization (tFBA). Our assumption is that if the activity of a gene drastically changes from one condition to the other, the flux through the reaction controlled by that gene will change accordingly. We allow these constraints to be violated, to account for posttranscriptional control and noise in the data. These up/down constraints are less stringent than the on/off constraints as previously proposed. Nevertheless, we obtain promising predictions, since many up/down constraints can be enforced. The potential of the proposed method, tFBA, is demonstrated through the analysis of fluxes in yeast under nine different cultivation conditions, between which approximately 5,000 regulatory up/down constraints can be defined. We show that changes in gene expression are predictive for changes in fluxes. Additionally, we illustrate that flux distributions obtained with tFBA better fit transcriptomics data than previous methods. Finally, we compare tFBA and FBA predictions to show that our approach yields more biologically relevant results.

关键词： Gene expression data mixed-integer linear programming optimization function FBA

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CSCF: a chaotic sine cosine firefly algorithm for practical application problems

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NEURAL COMPUTING & APPLICATIONS 2021年第12期33卷 7011-7030页

作者： Hassan, Bryar A. Kurdistan Inst Strateg Studies & Sci Res Sulaimani Iraq Sulaimani Polytech Univ Tech Coll Informat Dept Comp Networks Sulaimani Iraq

Recently, numerous meta-heuristic-based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity, etc. However, several optimization algorithms namely firefly algorithm, sine-cosine algorithm, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. So to overcome such shortcomings, this paper aims in developing a novel chaotic sine-cosine firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine-cosine algorithm and the firefly algorithms is integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping are selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.

关键词： CSCF Engineering design problems Variants Chaotic maps optimization function

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GAN-Based Face Attribute Editing

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IEEE ACCESS 2020年 8卷 34854-34867页

作者： Liu, Shuang Li, Dan Cao, Tianchi Sun, Yuke Hu, Yingsong Ji, Junwen Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China

Recently, a variety of methods using the Generative Adversarial Network (GAN) for face editing have been proposed. However, the existing methods cannot control the editing content of the face elements according to the user-specified attributes or need to train a conditional GAN for editing tasks, which means it is difficult to add new attributes in the future. In this paper, a method to edit face attributes by editing the latent variable with the help of a pre-trained unconditional GAN and a linear classification model is proposed. In particular, face attribute editing is divided into two separate stages: Firstly, based on the optimization function, the generative model does a latent variable search to generate a high-quality face image that is similar to the input image. Secondly, by editing the latent variable of the GAN, the attribute of the generated face image can be modified indirectly, so it is almost unaffected by the training process and network structure of GAN, which means it is a flexible method for nearly all GAN network. Images of the FFHQ dataset are edited by attribute labels defined in Celeba dataset for experiments. These experiments prove that our method can edit a variety of face images that vary with race, gender, age, and camera shooting angle. The overall quality of the edited image is not inferior to the other face attribute editing method, and attribute classification for edited image shows a 92.6% attribute editing success rate of the proposed method.

关键词： Face Gallium nitride Generative adversarial networks Training optimization Mouth Machine learning Face attribute editing latent variable search optimization function generative adversarial network

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