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  • Open Access


    Research on Maximum Return Evaluation of Human Resource Allocation Based on Multi-Objective Optimization

    Hong Zhu1,2,*

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 741-748, 2020, DOI:10.32604/iasc.2020.010108

    Abstract In this paper, a human resource allocation method based on the multi-objective hybrid genetic algorithm is proposed, which uses the multi-stage decision model to resolve the problem. A task decision is the result of an interaction under a set of conditions. There are some available decisions in each stage, and it is easy to calculate their immediate effects. In order to give a set of optimal solutions with limited submissions, a multi-objective hybrid genetic algorithm is proposed to solve the combinatorial optimization problems, i.e. using the multiobjective hybrid genetic algorithm to find feasible solutions at all stages and the bilateral… More >

  • Open Access


    A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

    Eslam Mohammed Abdelkader1,2,*, Osama Moselhi3, Mohamed Marzouk4, Tarek Zayed5

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 643-661, 2020, DOI:10.32604/iasc.2020.010100

    Abstract Image segmentation is one of the fundamental stages in computer vision applications. Several meta-heuristics have been applied to solve the segmentation problems by extending the Otsu and entropy functions. However, no single-objective function can optimally handle the diversity of information in images besides the multimodality issues of gray-level images. This paper presents a self-adaptive multi-objective optimization-based method for the detection of crack images in reinforced concrete bridges. The proposed method combines the flexibility of information theory functions in addition to the invasive weed optimization algorithm for bi-level thresholding. The capabilities of the proposed method are demonstrated through comparisons with singleobjective… More >

  • Open Access


    Impact of Fuzzy Normalization on Clustering Microarray Temporal Datasets Using Cuckoo Search

    Swathypriyadharsini P1,∗, K.Premalatha2,†

    Computer Systems Science and Engineering, Vol.35, No.1, pp. 39-50, 2020, DOI:10.32604/csse.2020.35.039

    Abstract Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding… More >

  • Open Access


    Global Levy Flight of Cuckoo Search with Particle Swarm Optimization for Effective Cluster Head Selection in Wireless Sensor Network

    Vijayalakshmi. K1,*, Anandan. P2

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 303-311, 2020, DOI:10.31209/2020.100000165

    Abstract The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of Wireless Sensor Networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. Several key issues in the WSN design can be addressed using the Multi-Objective Optimization (MOO) Algorithms. The selection of the Cluster Head is a NP Hard optimization problem in nature. The CH selection is also challenging as the sensor nodes are organized in clusters. Through partitioning of network, the consumption of… More >

  • Open Access


    Multi-Objective Optimization of Slow Moving Inventory System Using Cuckoo Search

    Achin Srivastav, Sunil Agrawal

    Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 343-350, 2018, DOI:10.1080/10798587.2017.1293891

    Abstract This paper focuses on the development of a multi-objective lot size–reorder point backorder inventory model for a slow moving item. The three objectives are the minimization of (1) the total annual relevant cost, (2) the expected number of stocked out units incurred annually and (3) the expected frequency of stockout occasions annually. Laplace distribution is used to model the variability of lead time demand. The multi-objective Cuckoo Search (MOCS) algorithm is proposed to solve the model. Pareto curves are generated between cost and service levels for decision-makers. A numerical problem is considered on a slow moving item to illustrate the… More >

  • Open Access


    Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001

    Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we… More >

  • Open Access


    A Non-probabilistic Reliability-based Optimization of Structures Using Convex Models

    Fangyi Li1,2, Zhen Luo3, Jianhua Rong1, Lin Hu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.95, No.6, pp. 453-482, 2013, DOI:10.3970/cmes.2013.095.453

    Abstract This paper aims to propose a non-probabilistic reliability-based multiobjective optimization method for structures with uncertain-but-bounded parameters. A combination of the interval and ellipsoid convex models is used to account for the different groups of uncertain parameters, in which the interval model accounts for uncorrelated parameters, while the ellipsoid model is applied to correlated parameters. The design is then formulated as a nested double-loop optimization problem. A multi-objective genetic algorithm is used in the out loop optimization to optimize the design vector for evaluating the objectives, and the Sequential Quadratic Programming (SQP) algorithm is applied in the inner loop to evaluate… More >

  • Open Access


    Direct Interval Multi-Objective Optimization Method for Uncertain Structures

    Guiping Liu*, Sheng Liu

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.4, pp. 92-92, 2019, DOI:10.32604/icces.2019.05523

    Abstract In engineering multi-objective optimization of structures, the parameters involved in the problems are usually given deterministic values. However, due to the presence of manufacturing and measurement errors, uncertainty inevitably exists in the geometrical properties of the structure, the material properties, the boundary conditions, etc. For uncertain problems, the interval optimization methods are widely used. They describe the uncertainty by intervals which only need to find the upper and lower bounds of the uncertain parameters instead of constructing the exact probability distribution function. However, in multi-objective optimization problems, if considered all the upper and lower bounds of the objective functions as… More >

  • Open Access


    A New Multi-objective Reliability-based Robust Design Optimization Method

    Zichun Yang1,2, Maolin Peng1,3,4, Yueyun Cao1, Lei Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.98, No.4, pp. 409-442, 2014, DOI:10.3970/cmes.2014.098.409

    Abstract A new multi-objective reliability-based robust design optimization (M ORBRDO) model is proposed which integrats the multi-objective robustness, the reliability sensitivity robustness and the six sigma robustness design idea. The pure-quadratic polynomial functions are adopted to fit the performance objective functions (POF) and the ultimate limited state functions (ULSF) of the structure. Based on the ULSF and the checking point method, the equations of the first order reliability index are calculated. The mapping transformation method is employed when the non-normal distribution variables are included. According to the POF and the Taylor series expansion method, the equations of mean value and standard… More >

  • Open Access


    Multi-Objective Optimization of a Fluid Structure Interaction Benchmarking

    M. Razzaq1, C. Tsotskas2, S. Turek1, T. Kipouros2, M. Savill2, J. Hron3

    CMES-Computer Modeling in Engineering & Sciences, Vol.90, No.4, pp. 303-337, 2013, DOI:10.3970/cmes.2013.090.303

    Abstract The integration and application of a new multi-objective tabu search optimization algorithm for Fluid Structure Interaction (FSI) problems are presented. The aim is to enhance the computational design process for real world applications and to achieve higher performance of the whole system for the four considered objectives. The described system combines the optimizer with a well established FSI solver which is based on the fully implicit, monolithic formuFlation of the problem in the Arbitrary Lagrangian-Eulerian FEM approach. The proposed solver resolves the proposed fluid-structure interaction benchmark which describes the self-induced elastic deformation of a beam attached to a cylinder in… More >

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