Constrained optimization by ε constrained particle swarm optimizer with ε-level control

Abstract

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