Constrained optimization using penalty function method combined with genetic algorithm

An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization

In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach.

A rough penalty genetic algorithm for constrained optimization

Many real-world issues can be formulated as constrained optimization problems and solved using evolutionary algorithms with penalty functions. To effectively handle constraints, this study hybridizes a novel genetic algorithm with the rough set theory, called the rough penalty genetic algorithm (RPGA), with the aim to effectively achieve robust solutions and resolve constrained optimization pro...

Constrained Evolutionary Optimization - the penalty function approach

Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization

At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constrained optimization problem with some prescribed tolerance. In the continuous world, using exact arithmetic, this subproblem is always solvable. Therefore, the possibility of finishing the subproblem resolution without satisfying the theoretical stopping conditions is not contemplated in usual converg...

Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis

Genetic algorithms (GAs) have been successfully applied to numerical optimization problems. Since GAs are usually designed for unconstrained optimization, they have to be adapted to tackle the constrained cases, i.e. those in which not all representable solutions are valid. In this work we experimentally compare 5 ways to attain such adaptation. Our analysis relies on the usual method of select...