Constrained optimization with Election campaign algorithm

In this paper, we present a new method using Election campaign algorithm (ECA) combining with the dynamic penalty method to solve the nonlinear constrained optimization problems. The proposed approaches are validated using several examples taken from the optimization literature, and our results are compared with those obtained by particle swarm optimization algorithm (PSO). Our comparative study indicates that ECA verifies its good performance when dealing with constrained optimization problems with constraints.