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  • Open Access


    Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

    Hongshang Xu1, Bei Dong1,2,*, Xiaochang Liu1, Xiaojun Wu1,2

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 185-202, 2023, DOI:10.32604/iasc.2023.041177

    Abstract Deep neural networks often outperform classical machine learning algorithms in solving real-world problems. However, designing better networks usually requires domain expertise and consumes significant time and computing resources. Moreover, when the task changes, the original network architecture becomes outdated and requires redesigning. Thus, Neural Architecture Search (NAS) has gained attention as an effective approach to automatically generate optimal network architectures. Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity. A myriad of research has revealed that network performance and structural complexity are often positively correlated. Nevertheless, complex network structures will bring… More >

  • Open Access


    Crashworthiness Design and Multi-Objective Optimization of Bionic Thin-Walled Hybrid Tube Structures

    Pingfan Li, Jiumei Xiao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 999-1016, 2024, DOI:10.32604/cmes.2023.044059

    Abstract Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties. However, issues such as high initial stress and low energy-absorbing efficiency arise. This study proposes a novel energy-absorbing structure in which a straight tube is combined with a conical tube and a bamboo-inspired bulkhead structure is introduced. This configuration allows the conical tube to flip outward first and then fold together with the straight tube. This deformation mode absorbs more energy and less peak force than the conical tube sinking and flipping inward. Through finite element numerical simulation, the specific More >

  • Open Access


    Multi-Objective Image Optimization of Product Appearance Based on Improved NSGA-Ⅱ

    Yinxue Ao1, Jian Lv1,*, Qingsheng Xie1, Zhengming Zhang2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3049-3074, 2023, DOI:10.32604/cmc.2023.040088

    Abstract A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation (DNSGA-II) strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product. First, the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users; then, the product multidimensional scale analysis is applied to classify the research objects, and again the reference samples are screened by the semantic differential method, and the samples are parametrized in two dimensions by using elliptic Fourier analysis; finally, the… More >

  • Open Access


    Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

    Guobin He*, Rui Su, Jinxin Yang, Yuanping Huang, Huanlin Chen, Donghui Zhang, Cangtao Yang, Wenwen Li

    Energy Engineering, Vol.120, No.9, pp. 2133-2154, 2023, DOI:10.32604/ee.2023.029007

    Abstract In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization, multi-resource penetration in active distribution networks has been advancing fiercely. In particular, distributed generation (DG) based on renewable energy is critical for active distribution network operation enhancement. To comprehensively analyze the accessing impact of DG in distribution networks from various parts, this paper establishes an optimal DG location and sizing planning model based on active power losses, voltage profile, pollution emissions, and the economics of DG costs as well as meteorological conditions. Subsequently, multi-objective particle swarm optimization (MOPSO) is… More >

  • Open Access


    Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

    Jingxiao Han1, Chuanzhao Zhang2, Lu Wang3,*, Zengjun Chang1, Qing Zhao1, Ying Shi4, Jiarui Wu5, Xiangfei Kong3

    Energy Engineering, Vol.120, No.9, pp. 1991-2011, 2023, DOI:10.32604/ee.2023.029749

    Abstract For heating systems based on electricity storage coupled with solar energy and an air source heat pump (ECSA), choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency. In this paper, four cities in three climatic regions in China were selected, namely Nanjing in the hot summer and cold winter region, Tianjin in the cold region, Shenyang and Harbin in the severe cold winter region. The levelized cost of heat (LCOH) was used as the economic evaluation index, and the energy consumption and emissions of different pollutants… More > Graphic Abstract

    Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

  • Open Access


    Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

    Qasem Al-Tashi1,*, Tareq M Shami2, Said Jadid Abdulkadir3, Emelia Akashah Patah Akhir3, Ayed Alwadain4, Hitham Alhussain3, Alawi Alqushaibi3, Helmi MD Rais3, Amgad Muneer1, Maliazurina B. Saad1, Jia Wu1, Seyedali Mirjalili5,6,7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1937-1966, 2023, DOI:10.32604/csse.2023.039788

    Abstract The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized. While it is a multi-objective problem, current methods tend to treat feature selection as a single-objective optimization task. This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase (LMuMOGWO) for tackling feature selection problems. The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer (MOGWO): a Lévy flight and a mutation operator. The Lévy flight, a type of… More >

  • Open Access


    Multi-Objective Optimization of Traffic Signal Timing at Typical Junctions Based on Genetic Algorithms

    Zeyu Zhang1, Han Zhu1, Wei Zhang1, Zhiming Cai2,*, Linkai Zhu2, Zefeng Li2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1901-1917, 2023, DOI:10.32604/csse.2023.039395

    Abstract With the rapid development of urban road traffic and the increasing number of vehicles, how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities. Therefore, in this paper, a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed. Specifically, a typical urban intersection was selected as the research object, and drivers’ acceleration habits were taken into account. What’s more, the shortest average delay time, the least average number of stops, and the maximum capacity of the intersection… More >

  • Open Access


    Research Progress of Aerodynamic Multi-Objective Optimization on High-Speed Train Nose Shape

    Zhiyuan Dai, Tian Li*, Weihua Zhang, Jiye Zhang

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1461-1489, 2023, DOI:10.32604/cmes.2023.028677

    Abstract The aerodynamic optimization design of high-speed trains (HSTs) is crucial for energy conservation, environmental preservation, operational safety, and speeding up. This study aims to review the current state and progress of the aerodynamic multi-objective optimization of HSTs. First, the study explores the impact of train nose shape parameters on aerodynamic performance. The parameterization methods involved in the aerodynamic multiobjective optimization of HSTs are summarized and classified as shape-based and disturbance-based parameterization methods. Meanwhile, the advantages and limitations of each parameterization method, as well as the applicable scope, are briefly discussed. In addition, the NSGA-II algorithm,… More >

  • Open Access


    Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

    Fadwa Alrowais1, Jaber S. Alzahrani2, Radwa Marzouk1, Abdullah Mohamed3, Gouse Pasha Mohammed4,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6145-6160, 2023, DOI:10.32604/cmc.2023.038300

    Abstract Combined Economic and Emission Dispatch (CEED) task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs. The disadvantage of the conventional method is its incapability to avoid falling in local optimal, particularly when handling nonlinear and complex systems. Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box. Therefore, this paper focuses on the design of an improved sand cat optimization algorithm based CEED (ISCOA-CEED) technique. The ISCOA-CEED technique majorly concentrates on reducing fuel costs More >

  • Open Access



    Ali Chitsazana , Georg Kleppa, Mohammad Esmaeil Chitsazanb, Birgit Glasmacherc

    Frontiers in Heat and Mass Transfer, Vol.18, pp. 1-6, 2022, DOI:10.5098/hmt.18.17

    Abstract For the optimization of the impinging round jet, the pressure force coefficient and drying energy consumption on the moving curved surface are set as the objective functions to be minimized simultaneously. SHERPA search algorithm is used to search for the optimal point from multiple objective tradeoff study (Pareto Front) method. It is found that the pressure force coefficient on the impingement surface is highly dependent on the jet to surface distance and jet angle, while the drying energy consumption is highly dependent on the jet to jet spacing. Generally, the best design study during the More >

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