The applicability of the proposed hybrid BBO DKHA to solve the constrained optimization problem
In this paper, to obtain the fitness function for proposed GA, the constrained optimization problem
(here, it is the maximization of constrained Redundancy allocation problem) is converted to an unconstrained optimization problem for handling of constraints by a new penalty function technique.
x] be an optimal solution of the constrained optimization problem
The constrained optimization problem
is resolved by standard and improved PSO algorithms according to the following parameters:
In this paper, an improved ant colony algorithm for solving continuous optimization problems has been proposed and applied to solve the water-reusing network optimization, which is a high-dimensional and non-linear constrained optimization problem
The proposed discriminant fusion strategy has two advantages: 1) fused data has the largest class discriminant owing to obtaining the fusion coefficients by solving a constrained optimization problem
created in the average margin criterion; 2) fusion coefficients are unique owing to they are equal to the elements of the eigenvector of one eigenvalue problem transformed by the above optimization problem.
By introducing a Lagrange multiplier [lambda] [member of] S this constrained optimization problem
is transformed into the unconstrained problem of finding the saddle-point (u, [lambda]) [member of] V x S of the Lagrangian functional
Let us concentrate on the efficient solution of the constrained optimization problem
A simple multimembered evolution strategy to solve constrained optimization problems
Constrained optimization problems
are very common, for example, in engineering applications, and therefore it is important to be able to deal with them efficiently.
The constraint-handling operator is only active for constrained optimization problems
Three chapters on stochastic modeling review the main stochastic methods for solving continuous non-convex constrained optimization problems
, identify linear time-varying systems using Kalman filters, and introduce an IDE for designing fuzzy rules.