Evaluation of penalty function methods for constrained optimization using particle swarm optimization

Solving complex problems with higher dimensions involving many constraints is often a very challenging task. While solving multidimensional problems with particle swarm optimization involving several constraint factors, the penalty function approach is widely used. This paper provides a comprehensive survey of some of the frequently used constraint handling techniques currently used with particle swarm optimization. In this paper some of the penalty functional approaches for solving evolutionary algorithms are discussed and a comparative study is being performed with respect to various benchmark problems to assess their performance.