The Experts below are selected from a list of 7179 Experts worldwide ranked by ideXlab platform

Shenggen Zheng - One of the best experts on this subject based on the ideXlab platform.

  • qpso cd quantum behaved particle swarm optimization algorithm with Cauchy Distribution
    Quantum Information Processing, 2020
    Co-Authors: Amandeep Singh Bhatia, Mandeep Kaur Saggi, Shenggen Zheng
    Abstract:

    Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy Distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analyzed and compared with the classical PSO. It has been proved that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results shown that QPSO associated with Cauchy Distribution and natural selection strategy outperforms other variants in context of stability and convergence.

Amandeep Singh Bhatia - One of the best experts on this subject based on the ideXlab platform.

  • qpso cd quantum behaved particle swarm optimization algorithm with Cauchy Distribution
    Quantum Information Processing, 2020
    Co-Authors: Amandeep Singh Bhatia, Mandeep Kaur Saggi, Shenggen Zheng
    Abstract:

    Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy Distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analyzed and compared with the classical PSO. It has been proved that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results shown that QPSO associated with Cauchy Distribution and natural selection strategy outperforms other variants in context of stability and convergence.

Mandeep Kaur Saggi - One of the best experts on this subject based on the ideXlab platform.

  • qpso cd quantum behaved particle swarm optimization algorithm with Cauchy Distribution
    Quantum Information Processing, 2020
    Co-Authors: Amandeep Singh Bhatia, Mandeep Kaur Saggi, Shenggen Zheng
    Abstract:

    Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy Distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analyzed and compared with the classical PSO. It has been proved that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results shown that QPSO associated with Cauchy Distribution and natural selection strategy outperforms other variants in context of stability and convergence.

Indranil Ghosh - One of the best experts on this subject based on the ideXlab platform.

  • kumaraswamy half Cauchy Distribution characterizations and related results
    International Journal of Statistics and Probability, 2015
    Co-Authors: Gholamhossein Hamedani, Indranil Ghosh
    Abstract:

    We present various characterizations of a recently introduced Distribution (Ghosh 2014), called KumaraswamyHalf- Cauchy Distribution based on: (i) a simple relation between two truncated moments; (ii) truncated moment of certain function of the 1 st order statistic; (iii) truncated moment of certain function of the random variable; (iv) hazard function; (v) Distribution of the 1 st order statistic; (vi) via record values. We also provide some remarks on

  • the kumaraswamy half Cauchy Distribution properties and applications
    Journal of Statistical Theory and Applications, 2014
    Co-Authors: Indranil Ghosh
    Abstract:

    In this article, based on the half-Cauchy Distribution, we propose a new Distribution called KumaraswamyHalf-Cauchy Distribution. Various explicit expressions for it’s moments, generating and quantile functions, mean deviations, reliability parameter, density function of the order statistics and their moments are provided. We consider the method of maximum likelihood to estimate the model parameters. For illustrative purposes, a real life data set is considered as an application of our new Distribution.

Hidetaka Nambo - One of the best experts on this subject based on the ideXlab platform.

  • improved optimization of numerical association rule mining using hybrid particle swarm optimization and Cauchy Distribution
    International Journal of Electrical and Computer Engineering, 2019
    Co-Authors: Imam Tahyudin, Hidetaka Nambo
    Abstract:

    Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy Distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.

  • the rules determination of numerical association rule mining optimization by using combination of pso and Cauchy Distribution
    International Conference on Management Science and Engineering, 2017
    Co-Authors: Imam Tahyudin, Hidetaka Nambo
    Abstract:

    One of the optimization methods to solve the numerical association rule mining problem is particle swarm optimization (PSO). This method is popularly used in various fields such as in the job scheduling problem, evaluating stock market, inferring gen regulatory networks and numerical association rule mining optimization. The weakness of the PSO is often premature for searching the optimal solution because it traps in local optima when the best particle is being searched in every iteration. Combining the PSO with Cauchy Distribution for numerical association rule mining problem (PARCD) is a solution because it is robust for finding the optimal solution in a large neighborhood. The important point in this proposed method is particle representation which to know the association between one attribute to another. Therefore, this study has the aim to determinate rules of numerical association rule mining and also to calculate the multi-objective function using combination of PSO and Cauchy Distribution. The results show that all of them explain every attribute to formulate the rule well. In addition, the multi-objective function value of PARCD method generally produces results which better than the previous method, MOPAR.

  • the combination of evolutionary algorithm method for numerical association rule mining optimization
    Advances in intelligent systems and computing, 2017
    Co-Authors: Imam Tahyudin, Hidetaka Nambo
    Abstract:

    The numerical problem of association rule mining is an updated issue. Numerous authors propose some methods to solved it. A number of them are using the optimization approach by Particle Swarm Optimization (PSO). The problem is that the PSO trapped in local optima when searched the best particle in every iteration. Many researchers solved this problem by combining with Cauchy Distribution because it is tremendous for searching in a large neighborhood. Hence, that combination will be implemented to accomplish the numerical association rule mining problem for some objective functions such as confidence, comprehensibility, interestingness. Based on the result the multi-objective of PSO for Numerical Association Rule Mining Problem with Cauchy Distribution (PARCD) showed the better result than the method of Multi-objective Particle Swarm Optimization for Association Rule Mining (MOPAR).