In this study, epsilon constrained particle swarm optimizer ePSO, which is the combination of the epsilon constrained method and particle swarm optimization, is proposed to solve constrained optimization problems.
The epsilon constrained methods can convert algorithms for unconstrained problems to algorithms for constrained problems using the epsilon level comparison, which compares the search points based on the constraint violation of them.
In the ePSO, the agents who satisfy the constraints move to optimize the objective function and the agents who don't satisfy the constraints move to satisfy the constraints.
Also, the way of controlling epsilon-level is given to solve problems with equality constraints.
The effectiveness of the ePSO is shown by comparing the ePSO with GENOCOP5.0 on some nonlinear constrained problems with equality constraints.