Electromagnetism-like Augmented Lagrangian Algorithm for Global Optimization
نویسندگان
چکیده
This paper presents an augmented Lagrangian algorithm to solve continuous constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is solved by a population-based method that uses an electromagnetism-like mechanism to move points towards optimality. Benchmark problems are solved in a performance evaluation of the proposed augmented Lagrangian methodology. A comparison with a well-known technique is also reported.
منابع مشابه
A Stochastic Augmented Lagrangian Equality Constrained-Based Algorithm for Global Optimization
This paper presents a numerical study of a stochastic augmented Lagrangian algorithm to solve continuous constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is...
متن کاملGlobal linear convergence of an augmented Lagrangian algorithm for solving convex quadratic optimization problems
We consider an augmented Lagrangian algorithm for minimizing a convex quadratic function subject to linear inequality constraints. Linear optimization is an important particular instance of this problem. We show that, provided the augmentation parameter is large enough, the constraint value converges globally linearly to zero. This property is viewed as a consequence of the proximal interpretat...
متن کاملGlobal Linear Convergence of an Augmented Lagrangian Algorithm to Solve Convex Quadratic Optimization Problems
We consider an augmented Lagrangian algorithm for minimizing a convex quadratic function subject to linear inequality constraints. Linear optimization is an important particular instance of this problem. We show that, provided the augmentation parameter is large enough, the constraint value converges globally linearly to zero. This property is viewed as a consequence of the proximal interpretat...
متن کاملMultiplier Algorithm Based on A New Augmented Lagrangian Function
In this paper, for nonconvex optimization problem with both equality and inequality constrains, we introduce a new augmented Lagrangian function and propose the corresponding multiplier algorithm. The global convergence is established without requiring the boundedness of multiplier sequences. In particular, if the algorithm terminates in finite steps, then we obtain a KKT point of the primal pr...
متن کاملAugmented Downhill Simplex a Modified Heuristic Optimization Method
Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm; so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, rand...
متن کامل