Discrete-continuous configuration optimization methods for structures using the harmony search algorithm. Key Engineering Materials 2006; 324-325, 1293-1296.Lee, K.S., Choi, C.S.: Discrete-continuous configuration optimization methods for structures using the harmony search algorithm. Key Engineering ...
To solve the exact projection problem on the group model, which is known to be equivalent to the maximum weight coverage problem, we use discrete optimization methods based on dynamic programming and Benders’ decomposition. The head- and tail-approximations are derived by a classical greedy-method...
The least squares method minimizes the error between the frequency responses of the continuous-time and discrete-time systems up to the Nyquist frequency using a vector-fitting optimization approach. This method is useful when you want to capture fast system dynamics but must use a larger sample ...
A discrete-continuous optimization problem is defined: genetic algorithms are used to determine the position of the actuators along the structure, and classical optimization techniques are performed in order to obtain the controller gains. The goal is to minimize the control effort applied to the ...
evolutionstrategyforoptimizationofcontinuousvariables. Objectiveofthisstudy:ToextendDifferentialEvolutionalgorithmformixed-discrete- continuousnon-linearoptimizationsubjecttomultiplenon-linearconstraints Requireshandlingtechniquesforall •continuousvariables •integervariables •discretevariables •boundaryconstraints •mu...
Some solution generators work well for discrete problems, but may perform poorly for continuous optimization; some are good for both. In the main 4-method flow-path, paths 0, 2, and 3 feature solution generators that solve discrete problems well - they are staple solution generators, while pat...
Dynamical methods for solving large-scale discrete and continuous optimization problemsDynamical methods for obtaining the global optimal solution of general optimization problems having closed form or black box objective functions, including the steps of first finding, in a deterministic manner, one local...
Motivated by the efficient algorithm of simultaneous perturbation stochastic approximation (SPSA) for continuous stochastic optimization problems, we introduce the middle point discrete simultaneous perturbation stochastic approximation (DSPSA) algorithm for the stochastic optimization of a loss function defined ...
structural optimizationIn this study, the evolution strategy, which is one of the evolutionary algorithms, is modified to solve mixeddiscrete optimization problems. Three approaches are proposed for handling discrete variables. The first approach is to treat discrete variables as continuous variables and ...
describes the main algorithmic approaches for integer-constrained network problems, such as branch-and-bound, Lagrangian relaxation and subgradient optimization, genetic algorithms, tabu search, simulated annealing, and rollout algorithms develops the main methods for nonlinear network problems, such as conve...