Performance of Optimization Algorithms Applied to Large Nonlinear Constrained Problems

A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems

We contribute improvements to a Lagrangian dual solution approach applied to large-scale optimization problems whose objective functions are convex, continuously differentiable and possibly nonlinear, while the nonrelaxed constraint set is compact but not necessarily convex. Such problems arise, for example, in the split-variable deterministic reformulation of stochastic mixed-integer optimizat...

Evolutionary Algorithms for Constrained Parameter Optimization Problems

Evolutionary computation techniques have received a lot of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a signi cant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently several methods have been proposed for han...

A Survey of Swarm Algorithms Applied to Discrete Optimization Problems

Swarm-based algorithms are inspired by the behavior of some social living beings, such as ants, termites, birds, and fishes. Self-organization and decentralized control are remarkable features of swarm-based systems that, such as in nature, lead to an emergent behavior. Emergent behavior is a property that emerges through local interactions among system components and it is not possible to be a...

Global optimization algorithms for bound constrained problems

Constrained genetic algorithms and their applications in nonlinear constrained optimization

This paper presents a problem-independent framework that uni es various mechanisms for solving discrete constrained nonlinear programming (NLP) problems whose functions are not necessarily di erentiable and continuous. The framework is based on the rst-order necessary and su cient conditions in the theory of discrete constrained optimization using Lagrange multipliers. It implements the search ...