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Search Results (15)
  • Open Access


    Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

    Adnan Ashraf1, Abdulwahab Ali Almazroi2, Waqas Haider Bangyal3,*, Mohammed A. Alqarni4

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 191-206, 2022, DOI:10.32604/iasc.2022.015810

    Abstract Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population’s initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the… More >

  • Open Access


    An Accelerated Convergent Particle Swarm Optimizer (ACPSO) of Multimodal Functions

    Yasir Mehmood, Waseem Shahzad

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 91-103, 2019, DOI:10.31209/2018.100000017

    Abstract Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms most of the compared PSO… More >

  • Open Access


    Tracking Features in Image Sequences with Kalman Filtering, Global Optimization, Mahalanobis Distance and a Management Model

    Raquel R. Pinho1, João Manuel R. S. Tavares1

    CMES-Computer Modeling in Engineering & Sciences, Vol.46, No.1, pp. 51-76, 2009, DOI:10.3970/cmes.2009.046.051

    Abstract This work addresses the problem of tracking feature points along image sequences. In order to analyze the undergoing movement, an approach based on the Kalman filtering technique has been used, which basically carries out the estimation and correction of the features' movement in every image frame. So as to integrate the measurements obtained from each image into the Kalman filter, a data optimization process has been adopted to achieve the best global correspondence set. The proposed criterion minimizes the cost of global matching, which is based on the Mahalanobis distance. A management model is employed to manage the features being… More >

  • Open Access


    Matching Contours in Images through the use of Curvature, Distance to Centroid and Global Optimization with Order-Preserving Constraint

    Francisco P. M. Oliveira1, João Manuel R. S. Tavares1

    CMES-Computer Modeling in Engineering & Sciences, Vol.43, No.1, pp. 91-110, 2009, DOI:10.3970/cmes.2009.043.091

    Abstract This paper presents a new methodology to establish the best global match of objects' contours in images. The first step is the extraction of the sets of ordered points that define the objects' contours. Then, by using the curvature value and its distance to the corresponded centroid for each point, an affinity matrix is built. This matrix contains information of the cost for all possible matches between the two sets of ordered points. Then, to determine the desired one-to-one global matching, an assignment algorithm based on dynamic programming is used. This algorithm establishes the global matching of the minimum global… More >

  • Open Access


    Parameter Identification Method of Large Macro-Micro Coupled Constitutive Models Based on Identifiability Analysis

    Jie Qu1,2, Bingye Xu3, Quanlin Jin4

    CMC-Computers, Materials & Continua, Vol.20, No.2, pp. 119-158, 2010, DOI:10.3970/cmc.2010.020.119

    Abstract Large and complex macro-micro coupled constitutive models, which describe metal flow and microstructure evolution during metal forming, are sometimes overparameterized with respect to given sets of experimental datum. This results in poorly identifiable or non-identifiable model parameters. In this paper, a systemic parameter identification method for the large macro-micro coupled constitutive models is proposed. This method is based on the global and local identifiability analysis, in which two identifiability measures are adopted. The first measure accounts for the sensitivity of model results with respect to single parameters, and the second measure accounts for the degree of near-linear dependence of sensitivity… More >

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