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Chuangyin Dang - One of the best experts on this subject based on the ideXlab platform.

  • a deterministic annealing algorithm for approximating a solution of the max bisection problem
    Neural Networks, 2002
    Co-Authors: Chuangyin Dang, Jiye Liang
    Abstract:

    The min-bisection problem is an NP-hard combinatorial optimization problem. In this paper an equivalent linearly constrained continuous optimization problem is formulated and an algorithm is proposed for approximating its solution. The algorithm is derived from the introduction of a logarithmic-cosine barrier function, where the barrier parameter behaves as temperature in an annealing procedure and decreases from a sufficiently large positive number to zero. The algorithm searches for a better solution in a feasible descent direction, which has a desired property that lower and upper bounds are always satisfied automatically if the step length is a number between zero and one. We prove that the algorithm converges to at least a Local Minimum Point of the problem if a Local Minimum Point of the barrier problem is generated for a sequence of descending values of the barrier parameter with a limit of zero. Numerical results show that the algorithm is much more efficient than two of the best existing heuristic methods for the min-bisection problem, Kernighan-Lin method with multiple starting Points (MSKL) and multilevel graph partitioning scheme (MLGP).

  • a barrier function method for the nonconvex quadratic programming problem with box constraints
    Journal of Global Optimization, 2000
    Co-Authors: Chuangyin Dang
    Abstract:

    In this paper a barrier function method is proposed for approximating a solution of the nonconvex quadratic programming problem with box constraints. The method attempts to produce a solution of good quality by following a path as the barrier parameter decreases from a sufficiently large positive number. For a given value of the barrier parameter, the method searches for a Minimum Point of the barrier function in a descent direction, which has a desired property that the box constraints are always satisfied automatically if the step length is a number between zero and one. When all the diagonal entries of the objective function are negative, the method converges to at least a Local Minimum Point of the problem if it yields a Local Minimum Point of the barrier function for a sequence of decreasing values of the barrier parameter with zero limit. Numerical results show that the method always generates a global or near global Minimum Point as the barrier parameter decreases at a sufficiently slow pace.

  • approximating a solution of the s t max cut problem with a deterministic annealing algorithm
    Neural Networks, 2000
    Co-Authors: Chuangyin Dang
    Abstract:

    The s-t max-cut problem is an NP-hard combinatorial optimization problem. In this paper an equivalent linearly constrained continuous optimization problem is formulated and an algorithm is proposed for approximating its solution. The algorithm is derived from an application of a logarithmic barrier function, where the barrier parameter behaves as temperature in an annealing procedure and decreases to zero from a sufficiently large positive number satisfying that the barrier function is convex. The algorithm searches for a better solution in a feasible descent direction, which has a desired property that lower and upper bounds are always satisfied automatically if the step length is a number between zero and one. We prove that the algorithm converges to at least a Local Minimum Point if a Local Minimum Point of the barrier problem is generated for a sequence of descending values of the barrier parameter with zero limit. Numerical results show that the algorithm seems effective and efficient.

Tao Chen - One of the best experts on this subject based on the ideXlab platform.

  • back propagation neural network with adaptive differential evolution algorithm for time series forecasting
    Expert Systems With Applications, 2015
    Co-Authors: Lin Wang, Yi Zeng, Tao Chen
    Abstract:

    We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.ADE is used to search for global initial connection weights and thresholds of BPNN.The proposed ADE-BPNN is effective for improving forecasting accuracy. The back propagation neural network (BPNN) can easily fall into the Local Minimum Point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE-BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE-BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models.

Jiye Liang - One of the best experts on this subject based on the ideXlab platform.

  • a deterministic annealing algorithm for approximating a solution of the max bisection problem
    Neural Networks, 2002
    Co-Authors: Chuangyin Dang, Jiye Liang
    Abstract:

    The min-bisection problem is an NP-hard combinatorial optimization problem. In this paper an equivalent linearly constrained continuous optimization problem is formulated and an algorithm is proposed for approximating its solution. The algorithm is derived from the introduction of a logarithmic-cosine barrier function, where the barrier parameter behaves as temperature in an annealing procedure and decreases from a sufficiently large positive number to zero. The algorithm searches for a better solution in a feasible descent direction, which has a desired property that lower and upper bounds are always satisfied automatically if the step length is a number between zero and one. We prove that the algorithm converges to at least a Local Minimum Point of the problem if a Local Minimum Point of the barrier problem is generated for a sequence of descending values of the barrier parameter with a limit of zero. Numerical results show that the algorithm is much more efficient than two of the best existing heuristic methods for the min-bisection problem, Kernighan-Lin method with multiple starting Points (MSKL) and multilevel graph partitioning scheme (MLGP).

Yan Tan - One of the best experts on this subject based on the ideXlab platform.

  • mobile robots path planning based on evolutionary artificial potential fields approach
    International Conference on Computer Science and Electronics Engineering, 2013
    Co-Authors: Yujun Wang, Yan Tan
    Abstract:

    This paper presents a new way for mobile robots’ path planning which is based on the Evolutionary Artificial Potential Fields(EAPF) approach. The APF theory is a traditional method to plan path for a robot. The Evolutionary APF aims at helping a robot jump out of the Local Minimum Point. Using a virtual goal to produce extra force and fixing the direction of the repulsive force are combined to prompt the robot to escape from the obstacle in different situations. The simulation result shows that the evolutionary method is effective for solving the Local Minimum problem. Keywords-virtual goal; artifical potential fields; Local Minimum Point; matrix; path planning

Lin Wang - One of the best experts on this subject based on the ideXlab platform.

  • back propagation neural network with adaptive differential evolution algorithm for time series forecasting
    Expert Systems With Applications, 2015
    Co-Authors: Lin Wang, Yi Zeng, Tao Chen
    Abstract:

    We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.ADE is used to search for global initial connection weights and thresholds of BPNN.The proposed ADE-BPNN is effective for improving forecasting accuracy. The back propagation neural network (BPNN) can easily fall into the Local Minimum Point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE-BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE-BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models.