A New Efficient Transformation of the Generalized Vehicle Routing Problem into the Classical Vehicle Routing Problem
A new evolutionary algorithm for multiobjective optimization based on endocrine paradigm.
A new evolutionary algorithm for the multiobjective knapasck problem.
A new evolutionary optimization technique.
A New Exterior Point Algorithm for Linear Programming Problems
A new formulation and solution of the sequencing problem: mathematical model
A New Genetic Representation for Quadratic Assignment Problem
A new global optimization algorithm for solving generalized geometric programming.
A new heuristic for the quadratic assignment problem.
A new heuristic for the traveling salesman problem
A new method for nonsmooth convex optimization.
A new method for solving monotone generalized variational inequalities.
A new minimal point theorem in product spaces.
A new modeling and solution approach for the number partitioning problem.
A new non-interior continuation method for -NCP based on a SSPM-function
In this paper, we consider a new non-interior continuation method for the solution of nonlinear complementarity problem with -function (-NCP). The proposed algorithm is based on a smoothing symmetric perturbed minimum function (SSPM-function), and one only needs to solve one system of linear equations and to perform only one Armijo-type line search at each iteration. The method is proved to possess global and local convergence under weaker conditions. Preliminary numerical results indicate that...
A New Nonlinear Model for the Two-Dimensional Packing Problem
A new nonmonotone adaptive trust region algorithm
We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstrained optimization problems. This algorithm incorporates two novelties: it benefits from a radius dependent shrinkage parameter for adjusting the trust region radius that avoids undesirable directions and exploits a new strategy to prevent sudden increments of objective function values in nonmonotone trust region techniques. Global convergence of this algorithm is investigated under some mild conditions....
A new one-step smoothing newton method for second-order cone programming
In this paper, we present a new one-step smoothing Newton method for solving the second-order cone programming (SOCP). Based on a new smoothing function of the well-known Fischer-Burmeister function, the SOCP is approximated by a family of parameterized smooth equations. Our algorithm solves only one system of linear equations and performs only one Armijo-type line search at each iteration. It can start from an arbitrary initial point and does not require the iterative points to be in the sets...
A new optimized iterative method for solving -matrix linear systems
In this paper, we present a new iterative method for solving a linear system, whose coefficient matrix is an -matrix. This method includes four parameters that are obtained by the accelerated overrelaxation (AOR) splitting and using the Taylor approximation. First, under some standard assumptions, we establish the convergence properties of the new method. Then, by minimizing the Frobenius norm of the iteration matrix, we find the optimal parameters. Meanwhile, numerical results on test examples...
A new parameterized logarithmic kernel function for linear optimization with a double barrier term yielding the best known iteration bound
In this paper, we propose a large-update primal-dual interior point algorithm for linear optimization. The method is based on a new class of kernel functions which differs from the existing kernel functions in which it has a double barrier term. The investigation according to it yields the best known iteration bound for large-update algorithm with the special choice of its parameter and thus improves the iteration bound obtained in Bai et al. [El Ghami2004] for large-update algorithm.