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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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A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction

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GEOCARTO INTERNATIONAL 2024年第1期39卷

作者： Tan, Jiayao Yang, Chi Wang, Yuzhou Xiong, Hanxiang Ma, Chuanming China Univ Geosci Sch Environm Studies Wuhan Peoples R China Shanghai Jiao Tong Univ Sch Environm Sci & Engn Shanghai Peoples R China Eastern Inst Technol Eastern Inst Adv Study Ningbo Peoples R China

Landslide inventory incompleteness (LII) may significantly affect the model performance in landslide susceptibility mapping (LSM). However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. This hybrid model mitigates the drawbacks of data-heavy statistical approaches and subjective heuristic models by incorporating LII into weight determination. The findings show that the LEO-MAHP model demonstrates superior performance (AUROC = 0.809 and 0.805) over conventional statistical (AUROC = 0.714 and 0.770) and heuristic models (AUROC = 0.738 and 0.741) across different LII levels. We further discuss alternative LII solutions, proposing an updated landslide management strategy that accounts for climate change and human activities. Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM.

关键词： Hybrid model landslide susceptibility optimization function prevention and control incomplete landslide inventory

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Optimal reduced-order interval observer design for uncertain continuous-time linear systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第2期33卷 1043-1064页

作者： Liu, Longwen Xie, Wei Zhang, Langwen South China Univ Technol Coll Automat Sci & Technol Guangzhou Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China South China Univ Technol Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Guangzhou Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai Peoples R China

This article investigates the reduced-order interval observer (R-IO) design technique for continuous-time linear systems with unknown external disturbances and measurement noises. First, we propose a coupled R-IO structure with more design degrees of freedom, and it can be directly applied not only to solve the difficulty of the error system cooperativity construction but to relax the constraint on the sensor measurement noises. Second, the R-IO existence condition is derived as a set of matrix equations (MEs), and a complete solution, explicitly showing the available design parameters, to such an R-IO is further obtained by solving the MEs. Third, using the solution, an integrated optimization indicator of the R-IO performance is built as the valid selection mechanism of these parameters. Finally, the efficiency of the obtained results is illustrated through a numerical example and a practical example.

关键词： interval estimation optimization function parametric design reduced-order observer

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A robust and secured adaptive image watermarking using social group optimization

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VISUAL COMPUTER 2023年第10期39卷 4839-4854页

作者： Garg, Preeti Kishore, R. Rama Guru Gobind Singh Indraprastha Univ Univ Sch Informat Commun & Technol New Delhi India Delhi NCR KIET Grp Inst Ghaziabad India

Watermarking is the process of inserting concealed data into carrier data to authenticate the owner of the material. To achieve optimal performance, we present an intelligent system for watermarking that combines a meta-heuristic method with an embedding technique. The suggested work proposes a blind watermarking technique that embeds the watermark bits in the best location of discrete cosine transform blocks while taking advantage of the discrete wavelet transform's features. To safeguard the embedded watermark, a two-step security mechanism is used: first, the column values are shuffled using a proposed shuffling algorithm, and then the scrambled watermark is encrypted using the Arnold encryption scheme. The primary goal of any watermarking technology is to protect embedded data from various attacks while maintaining the carrier data's quality. By tailoring the embedding site for watermark encapsulation, the recommended technique achieves an acceptable balance of these two characteristics known as imperceptibility and resilience. A meta-heuristic algorithm based on human social behavior is employed to optimize the placement. The social group optimization (SGO) algorithm is a new member of the family of meta-heuristic algorithms. No attempt has been made to include the social group optimization technique into applications for watermark embedding, to our knowledge. The SGO method can assist in striking a balance between various watermarking qualities. To demonstrate the utility of the suggested method, it is compared to a variety of existing watermarking techniques. The approach presented here is a robust solution that may be applied to a wide variety of multimedia applications, including telemedicine, media distribution, and security systems.

关键词： Digital watermarking Meta-heuristic optimization optimization function Social group optimization Adaptive locations

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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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Wideband Characterization of Single Parabolic Cylindrical Compact Range for Linear Array Feeds 14

Wideband Characterization of Single Parabolic Cylindrical Co...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Yang, Ke Wang, Zhengpeng Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350353129;9798350353136

Compact antenna ranges test (CATR) are commonly used for antenna testing and radar cross section measurements. The single parabolic cylindrical reflector (SPCR) compact range with a linear array feed can enhance aperture efficiency while maintaining high-quality quiet zone performance, thereby reducing the cost of measuring large equipment. This paper investigates the wideband characteristics of SPCR compact range. Firstly, the system design of the SPCR compact range with a linear array feed is introduced. Next, the wideband optimization function and optimization methods are discussed. Full-wave simulations and system experiments in the sub-6G operating frequency band are then conducted. The designed compact range achieves a high-quality quiet zone with an aperture efficiency of 60-70%, demonstrating the effectiveness and feasibility of the proposed method.

关键词： Compact antenna ranges test (CATR) quiet zone linear array optimization function

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Real-time active power dispatch of virtual power plant based on distributed model predictive control

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ELECTRONICS LETTERS 2022年第23期58卷 872-875页

作者： Feng, Shuai Funding, Dongsheng Yang Zhou, Bowen Luo, Yanhong Li, Guangdi Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

With the increasing penetration of renewable energy, virtual power plants (VPP) reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in VPP has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control (DMPC), so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbour information. Simulation results show the feasibility and effectiveness of the proposed method.

关键词： scheduling multiagent system power grid power generation dispatch optimization function multi-agent systems optimisation power plants virtual power plant neighbour information distributed control real-time active power dispatch scheme real-time optimal scheduling problem power engineering computing power grids predictive control renewable energy distributed model predictive control

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An extension of optimal fashion capsule wardrobe construction by considering visual dissimilarity and number of good coordinates 10

An extension of optimal fashion capsule wardrobe constructio...

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10th International Symposium on Computing and Networking (CANDAR)

作者： Tanaka, Yuta Ozaki, Tomonobu Nihon Univ Grad Sch Integrated Basic Sci Setagaya Ward 3-25-40 Sakurajosui Tokyo 1568550 Japan Nihon Univ Dept Informat Sci Setagaya Ward 3-25-40 Sakurajosui Tokyo 1568550 Japan

ISBN: (纸本)9781665475327

A capsule wardrobe (CW in short) is a minimal collection of interchangeable clothes from which a lot of diverse and attractive fashion coordination (combinations of clothes) can be obtained. CWs are created for the purpose of strategic brand developments, personal self-expression, and so on. Since a certain level of expertise is required, it is hard for fashion beginners to make CWs. In order to support for the creation of high-quality CWs, a method using an optimization technique has been developed, recently. In the method, fashion images are converted into certain vocabulary through image processing techniques, and a quasi-optimal CW is then derived by submodular optimization with respect to the compatibility and versatility which are calculated based on the LDA(Latent Dirichlet Allocation) topic model. However, the two metrics of compatibility and versatility are not always sufficient to meet the various demands of users. In this study, we extend the optimization function of the existing method by incorporating two factors of visual dissimilarity and number of good coordinates. By this extension, we can expect to realize a more flexible method for high-quality CW creation.

关键词： Fashion capsule wardrobe Submodular optimization optimization function

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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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