Solving constrained optimization problems by the ε constrained particle swarm optimizer with adaptive velocity limit control

The epsilon constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using the epsilon level comparison that compares the search points based on the constraint violation of them. We proposed the epsilon constrained particle swarm optimizer epsilon PSO, which is the combination of the epsilon constrained method and particle swarm optimization. In the epsilon PSO, 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. But sometimes the velocity of agents becomes too big and they fly away from feasible region. In this study, to solve this problem, we propose to divide agents into some groups and control the maximum velocity of agents adaptively by comparing the movement of agents in each group. The effectiveness of the improved epsilon PSO is shown by comparing it with various methods on well known nonlinear constrained problems.