In this study, alpha constrained particle swarm optimizer aPSO, which is the combination of the alpha constrained method and particle swarm optimization, is proposed to solve constrained optimization problems.
The alpha constrained methods can convert algorithms for unconstrained problems to algorithms for constrained problems using the alpha level comparison, which compares the search points based on the satisfaction level of constraints.
In the aPSO, 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.
The effectiveness of the aPSO is shown by comparing the aPSO with GENOCOP5.0, and other PSO-based methods on some nonlinear constrained problems.