The Experts below are selected from a list of 14766 Experts worldwide ranked by ideXlab platform

Thomas Back - One of the best experts on this subject based on the ideXlab platform.

  • Equality Constraint handling for surrogate assisted constrained optimization
    Congress on Evolutionary Computation, 2016
    Co-Authors: Samineh Bagheri, Wolfgang Konen, Thomas Back
    Abstract:

    Real-world optimization problems are often subject to many Constraints. Often, as the volume of the feasible space gets smaller, the problems become more complex. The zero volume of feasible spaces for optimization problems with Equality Constraints makes them challenging. In this paper, we present an Equality Constraint handling approach embedded for the first time in a surrogate-assisted optimizer (SACOBRA). The proposed technique starts with an expanded feasible area which gradually shrinks to a volume close to zero. Several well-studied constrained test problems are used as benchmarks and the promising results in terms of efficiency and accuracy are compared with other state-of-the-art algorithms.

  • CEC - Equality Constraint handling for surrogate-assisted constrained optimization
    2016 IEEE Congress on Evolutionary Computation (CEC), 2016
    Co-Authors: Samineh Bagheri, Wolfgang Konen, Thomas Back
    Abstract:

    Real-world optimization problems are often subject to many Constraints. Often, as the volume of the feasible space gets smaller, the problems become more complex. The zero volume of feasible spaces for optimization problems with Equality Constraints makes them challenging. In this paper, we present an Equality Constraint handling approach embedded for the first time in a surrogate-assisted optimizer (SACOBRA). The proposed technique starts with an expanded feasible area which gradually shrinks to a volume close to zero. Several well-studied constrained test problems are used as benchmarks and the promising results in terms of efficiency and accuracy are compared with other state-of-the-art algorithms.

Samineh Bagheri - One of the best experts on this subject based on the ideXlab platform.

  • Equality Constraint handling for surrogate assisted constrained optimization
    Congress on Evolutionary Computation, 2016
    Co-Authors: Samineh Bagheri, Wolfgang Konen, Thomas Back
    Abstract:

    Real-world optimization problems are often subject to many Constraints. Often, as the volume of the feasible space gets smaller, the problems become more complex. The zero volume of feasible spaces for optimization problems with Equality Constraints makes them challenging. In this paper, we present an Equality Constraint handling approach embedded for the first time in a surrogate-assisted optimizer (SACOBRA). The proposed technique starts with an expanded feasible area which gradually shrinks to a volume close to zero. Several well-studied constrained test problems are used as benchmarks and the promising results in terms of efficiency and accuracy are compared with other state-of-the-art algorithms.

  • CEC - Equality Constraint handling for surrogate-assisted constrained optimization
    2016 IEEE Congress on Evolutionary Computation (CEC), 2016
    Co-Authors: Samineh Bagheri, Wolfgang Konen, Thomas Back
    Abstract:

    Real-world optimization problems are often subject to many Constraints. Often, as the volume of the feasible space gets smaller, the problems become more complex. The zero volume of feasible spaces for optimization problems with Equality Constraints makes them challenging. In this paper, we present an Equality Constraint handling approach embedded for the first time in a surrogate-assisted optimizer (SACOBRA). The proposed technique starts with an expanded feasible area which gradually shrinks to a volume close to zero. Several well-studied constrained test problems are used as benchmarks and the promising results in terms of efficiency and accuracy are compared with other state-of-the-art algorithms.

Fuwen Yang - One of the best experts on this subject based on the ideXlab platform.

  • Set-Membership Filtering for Discrete-Time Systems With Nonlinear Equality Constraints
    IEEE Transactions on Automatic Control, 2009
    Co-Authors: Fuwen Yang
    Abstract:

    In this technical note, the problem of set-membership filtering is considered for discrete-time systems with nonlinear Equality Constraint between their state variables. The nonlinear Equality Constraint is first linearized and transformed into a state linear Equality Constraint with two uncertain quantities related to linearizing truncation error and base point error. S-procedure method is then applied to merge all inequalities into one inEquality and the solution to the unconstrained set-membership filtering problem is provided. The set-membership filter with state Constraint is finally derived from projecting the unconstrained set-membership filter onto the constrained surface by using Finsler's Lemma. A time-varying linear matrix inEquality optimization based approach is proposed to design the set-membership filter with nonlinear Equality Constraint. A recursive algorithm is developed for computing the state estimate ellipsoid that guarantees to contain the true state. An illustrative example is provided to demonstrate the effectiveness of the proposed set-membership filtering with nonlinear Equality Constraint.

Wolfgang Konen - One of the best experts on this subject based on the ideXlab platform.

  • Equality Constraint handling for surrogate assisted constrained optimization
    Congress on Evolutionary Computation, 2016
    Co-Authors: Samineh Bagheri, Wolfgang Konen, Thomas Back
    Abstract:

    Real-world optimization problems are often subject to many Constraints. Often, as the volume of the feasible space gets smaller, the problems become more complex. The zero volume of feasible spaces for optimization problems with Equality Constraints makes them challenging. In this paper, we present an Equality Constraint handling approach embedded for the first time in a surrogate-assisted optimizer (SACOBRA). The proposed technique starts with an expanded feasible area which gradually shrinks to a volume close to zero. Several well-studied constrained test problems are used as benchmarks and the promising results in terms of efficiency and accuracy are compared with other state-of-the-art algorithms.

  • CEC - Equality Constraint handling for surrogate-assisted constrained optimization
    2016 IEEE Congress on Evolutionary Computation (CEC), 2016
    Co-Authors: Samineh Bagheri, Wolfgang Konen, Thomas Back
    Abstract:

    Real-world optimization problems are often subject to many Constraints. Often, as the volume of the feasible space gets smaller, the problems become more complex. The zero volume of feasible spaces for optimization problems with Equality Constraints makes them challenging. In this paper, we present an Equality Constraint handling approach embedded for the first time in a surrogate-assisted optimizer (SACOBRA). The proposed technique starts with an expanded feasible area which gradually shrinks to a volume close to zero. Several well-studied constrained test problems are used as benchmarks and the promising results in terms of efficiency and accuracy are compared with other state-of-the-art algorithms.

Yoshiko Hanada - One of the best experts on this subject based on the ideXlab platform.

  • Equality Constraint handling technique with variables grouping in ea for large scale global optimization
    Systems Man and Cybernetics, 2018
    Co-Authors: Yukiko Orito, Yoshiko Hanada
    Abstract:

    In the constrained or the unconstrained large scale global optimization problems, it is difficult to find the optimal solution by using most existing evolutionary algorithms. For the Equality constrained large scale global optimization problem, we propose the new technique that groups variables to each of subcomponents and handles the Equality Constraint by using trigonometric functions. In use of our technique, the evolutionary algorithms effectively find good feasible solutions without evolutionary stagnation because the transformed unconstrained search space consists only of feasible solutions. In numerical experiments, for a portfolio replication problem, we demonstrate the effectiveness of our technique.

  • Equality Constraint handling technique with various mapping points the case of portfolio replication problem
    Congress on Evolutionary Computation, 2015
    Co-Authors: Yukiko Orito, Yoshiko Hanada
    Abstract:

    For solving an Equality constrained optimization problem, it is difficult to find an optimal solution by using any evolutionary algorithms. We propose a new technique that handles an Equality Constraint in this paper. The technique transforms variables of solution on Equality constrained search space to them on unconstrained search space through trigonometrical functions. Thus, this paper presents the contribution that an evolutionary algorithm effectively finds good feasible solutions without evolutionary stagnation because an unconstrained space consists only of feasible solutions. However, our technique searches mapping points only on the part of constrained space because it cannot transform the constrained space to fully unconstrained space. Therefore, we expand such a space consisting of various mapping points by exchanging trigonometrical functions on EDA (Estimation of Distribution Algorithm). In numerical experiments, for portfolio replication problems, we demonstrate the effectiveness of our technique.

  • a new Equality Constraint handling technique unconstrained search space proposal for Equality constrained optimization problem
    2015
    Co-Authors: Yukiko Orito, Yoshiko Hanada, Shunsuke Shibata, Hisashi Yamamoto
    Abstract:

    For solving an Equality constrained optimization problem, it is difficult to search an optimal solution by using any evolutionary algorithms. We propose a new technique which removes an Equality Constraint in an optimization problem in this paper. The technique transforms an Equality constrained search space to an unconstrained search space for a portfolio replication problem. In numerical experiments, we show that evolutionary algorithms can generate good solutions in an unconstrained search space obtained by the technique.