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

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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Parametric optimization of a Reactive Distillation Column for the Ethyl Tert-Butyl Ether Production

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PERIODICA POLYTECHNICA-CHEMICAL ENGINEERING 2024年第1期68卷 116-123页

作者： Serpas, Carlos E. Garcia Samborskaya, Marina A. Mityanina, Olga E. Engn Chem Technol Ctr LLC Alekseya Belentza Str 9-1 Tomsk 634050 Russia Tomsk Polytech Univ Sch Earth Sci & Engn Dept Chem Engn Lenina Ave 30 Tomsk 634050 Russia

Combined processes in chemical industry are of current increasing interest, since they present a feasible solution for capital and operating expenses optimization. Multiplicities, which are specific to combined processes, cause certain control and design difficulties, thus, parametric sensitivity analysis and defining the range of existing multiplicities are the necessary part of the sustainable design and development of the combined process. Parametric and design optimization is the way of decreasing capital and operational costs. The paper discusses optimization of the design and operating parameters of an ethyl-tert-butyl ether synthesis reactive distillation column. The research aims to perform a parametric sensitivity analysis and explore existent multiple steady states of the process. The areas of multiple steady states are established, the optimal column design is developed, the optimal operation modes are determined. The procedure suggested in the study is universally applicable for design and optimization of other reactive distillation processes.

关键词： mathematical model optimization function parametric sensitivity reactive distillation synthesis ethyl tert -butyl ether

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Automatic tuning of segmentation parameters for tree crown delineation with VHR imagery

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GEOCARTO INTERNATIONAL 2021年第19期36卷 2241-2259页

作者： Sothe, Camile de Almeida, Claudia Maria Schimalski, Marcos Benedito Liesenberg, Veraldo Diaz, Pedro Achanccaray Natl Inst Space Res INPE Remote Sensing Div Sao Jose Dos Campos SP Brazil Santa Catarina State Univ UDESC Dept Forest Engn Lages SC Brazil Pontifical Catholic Univ Rio de Janeiro PUC Dept Elect Engn Rio De Janeiro RJ Brazil

In the case of tree species delineation with very high spatial resolution (VHR) images, is desirable that each segment corresponds to one individual tree crown (ITC). However, in order to have a segmentation algorithm that generates segments matching to ITCs, its parameters ought to be properly tuned. Aiming to avoid time-consuming trial-and-error procedures associated with this task, some initiatives for the automatic search of segmentation parameters have been developed, such as metaheuristic methods. The objective of this work was to test the automatic tuning of segmentation parameters of three segmentation algorithms for the delineation of ITCs belonging to a native endangered species in a subtropical forest area, comparing this method with the traditional trial-and-error approach. Two datasets (WorldView-2 and an orthoimage) and three segmentation algorithms (multiresolution, mean-shift and graph-based) were tested. For the automatic approach, a hybrid metaheuristic method was applied to accomplish the automatic search of parameters for the segmentation algorithms, while for the trial-and-error, a visual assessment was conducted for each set of parameters tested. Four supervised metrics were used to assess the quality of the segmentation results for the optimization approach and for the final set of parameters chosen in the trial-and-error approach. Results showed that none of the algorithms, datasets or approaches differ too much. The evaluation metrics values were lower, indicating that the reference ITCs polygons matched with the segmentation results. Despite the similar results, the automatic tuning of segmentation parameters proved to be a feasible alternative to reduce the subjectivity and the human effort in the choice of segmentation parameters as compared to the trial-and error approach.

关键词： Individual tree crown image segmentation object-based image analysis optimization function metaheuristic methods

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GIFM: An Image Restoration Method With Generalized Image Formation Model for Poor Visible Conditions

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Liang, Zheng Zhang, Weidong Ruan, Rui Zhuang, Peixian Li, Chongyi Anhui Univ Sch Internet Hefei 230039 Peoples R China Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Henan Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

Recently, image restoration has attracted considerable attention from researchers, and these methods generally restore degraded images based on the atmospheric scattering model (ATSM) and retinex model (RM). The two models only take into the single attenuation process during imaging, thereby introducing undesirable results. To deal with this issue, we propose an image restoration method based on a generalized image formation model (GIFM). First, unlike the existing image restoration methods, we rebuild a novel image formation model, which describes the light attenuation process that includes the light source-scene path and scene-sensor path. Second, we construct an objective optimization function to decompose a degraded image into a color distorted component and color corrected component, and an augmented Lagrange multiplier-based alternating direction minimization algorithm is provided to solve the optimization problem. Finally, we fully consider the advantages of the small-scale neighborhood and large-scale neighborhood in image restoration, and an image itself brightness-based weighted fusion strategy is proposed to balance brightness enhancement and contrast improvement. Extensive experiments on three image enhancement datasets show that our GIFM achieves better results than state-of-the-art methods. Experiments further suggest that our GIFM performs well for image restoration of extreme scenes, keypoint detection, object detection, and image segmentation.

关键词： Image restoration Atmospheric modeling Image color analysis Attenuation optimization Imaging Brightness Image formation model image restoration optimization function weighted fusion

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Method for an Effective Selection of Tools and Cutting Conditions during Precise Turning of Non-Alloy Quality Steel C45

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MATERIALS 2022年第2期15卷 505页

作者： Ivchenko, Oleksandr Ivanov, Vitalii Trojanowska, Justyna Zhyhylii, Dmytro Ciszak, Olaf Zaloha, Olha Pavlenko, Ivan Hladyshev, Dmytro Sumy State Univ Dept Mfg Engn Machines & Tools 2 Rymskogo Korsakova Str UA-40007 Sumy Ukraine Poznan Univ Tech Fac Mech Engn Piotrowo 3 PL-60965 Poznan Poland Sumy State Univ Dept Computat Mech 2 Rymskogo Korsakova Str UA-40007 Sumy Ukraine

The paper presents a constructing methodology for a modern approach to tools selection and solving the problem of assigning optimal cutting parameters for specific production conditions. The mathematical formulation determining the extreme values of the technological process optimality criteria is obtained. A system of technical and economic quality indicators for cutting tools is proposed. This system allows principles' implementation of decentralization and interoperability "Industry 4.0" via finite element modeling of the cutting process based on solving the problem of orthogonal free cutting modeling. The proposed methodology further usage is possible by creating a standardized database on the parameters of the tool: the adhesive component of the friction cutting coefficient for processing of a specific pair of cutting and tool materials (or tool coating material) and the impacts of the cutting-edge radius on cutting efficiency of a particular material.

关键词： edge cutting tool finite element model precision machining cutting process industrial growth simulation design optimization function friction coefficient

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Model Predictive Control of Unmanned Mine Vehicle Trajectory Tracking 40

Model Predictive Control of Unmanned Mine Vehicle Trajectory...

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40th Chinese Control Conference (CCC)

作者： Xin, Peng Wang, Zhiwen Sun, Hongtao Zhang, Bin Lanzhou Univ Technol Sch Elect Engn & Informat Engn Lanzhou 730050 Peoples R China Lanzhou Univ Technol Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Peoples R China Lanzhou Univ Technol Natl Demonstrat Ctr Expt Elect & Control Engn Edu Lanzhou 730050 Peoples R China Qufu Normal Univ Coll Engn Rizhao 276826 Peoples R China

ISBN: (纸本)9789881563804

Unmanned mine vehicle driving technology is a significant part of smart mining research. This paper proposes a model predictive control strategy for the planning path trajectory tracking problem of unmanned mine vehicles during mining and transportation. Firstly, we construct an optimized function with soft constraints. Secondly, the terminal equation constraint is introduced to design model predictive control optimization problem, thereby transforming the trajectory tracking problem of the unmanned mine vehicle into the solution of the optimization problem. Then the unmanned mine vehicle trajectory tracking model predictive controller is designed, and the stability of the closed loop system through the Lyapunov stability theory is proved. Finally, a simulation example is used to verify that the terminal equation constraint model predictive controller proposed in this paper has a good tracking effect in the process of unmanned mine vehicle trajectory tracking. The control strategy proposed in this paper has improved the effect of unmanned mine vehicle planning path tracking, and its the following speed and stability are enhanced.

关键词： Unmanned Mine Vehicle Driving Technology optimization function Model Predictive Control Trajectory Tracking

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Multiple-objective optimization of a reconfigurable assembly system via equipment selection and sequence planning

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第PartA期172卷

作者： Yang, Jiangxin Liu, Fan Dong, Yafei Cao, Yanlong Cao, Yanpeng Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310027 Peoples R China

With the development of society and technology, the modern manufacturing system is required to handle unpredictable and rapidly changing market demands during various demand periods. This is a new challenge to the production scheduling of the production line, which requires a faster and smarter response to changes in production demand. To meet the challenge, the reconfigurable manufacturing system (RMS) has emerged as a competitive and promising manufacturing model. The reconfigurable assembly system (RAS), a critical feature of RMS, can be seamlessly incorporated into multi-variety, variable-batch, multi-functional, and fast -delivery manufacturing modes, opening up the possibility of rapid changes for production scheduling. In this paper, we first build a RAS model that can more accurately represent scheduling problems that may occur in real-world industrial operations. More specifically, we consider the assembly process of products could be divided into a number of major steps which are performed in different stations and optimize the assembly sequence of products and the equipment selection within individual assembly stations. After that, three objective functions corresponding to the workload balance, reconfiguration/assembly cost, and lead time are proposed to jointly optimize the assembly sequence and the equipment selection in individual assembly stations. Finally, a hybrid particle swarm optimization (MOHPSO) method is utilized to solve the multiple -objective optimization problem, which combines the advantages of particle swarm optimization and the hybrid method. Experimental results show the superiority of the proposed MOHPSO method over other alternatives including MOPSO and NSGA-II, achieving lower Spacing Metric (SM) and Mean Ideal Distance (MID) indexes and is suitable for solving production scheduling problems.

关键词： Reconfigurable manufacturing system (RMS) Reconfigurable assembly system (RAS) optimization function Multiple-objective hybrid particle swarm optimization (MOHPSO)

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A unified total variation method for underwater image enhancement

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KNOWLEDGE-BASED SYSTEMS 2022年 255卷

作者： Ding, Xueyan Wang, Yafei Liang, Zheng Fu, Xianping Dalian Maritime Univ Informat Sci & Technol Sch Dalian 116026 Peoples R China Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China

Underwater images usually suffer from color casts and low contrast due to the absorption and scattering of light by the water medium. The degradation is caused not only by the light attenuation on the scene-sensor path but also by the light attenuation on the water surface-scene path. To eliminate the dual-path light attenuation, we propose a novel unified total variation method based on an extended underwater imaging model. Unlike previous variation-based methods that only consider light propagation along the scene-sensor path, we additionally include light propagation along the water surface-scene path in the underwater imaging model. In the proposed variational framework, we transform underwater image enhancement into two subproblems and construct different prior knowledge-guided optimization functions for them. The two subproblems aim to remove the light attenuation along the scene-sensor and surface-scene paths. Moreover, we present an alternating direction minimization algorithm based on an augmented Lagrange multiplier to address the opti-mization problems. The subjective and objective experimental results on underwater images with different attenuation characteristics demonstrate that the proposed method achieves good performance in underwater image enhancement. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Underwater image enhancement Unified total variation Light propagation 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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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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