Memetic Differential Evolution combined with Constraint Consensus method for solving COPs

Reaching feasible solutions in constrained optimization problems is a prime condition that requires the conversion of one or more infeasible individuals to feasible individuals. In this paper, to ensure the effective movement of infeasible individuals towards feasible region, we introduce a Constraint Consensus method within a Differential Evolution (DE) algorithm for solving constrained optimization problems. In addition, we use Sequential Quadratic Programming as a local search algorithm to speed up the convergence of the algorithm. The algorithm has been tested by solving 24 well-known benchmark problems. The experimental results show that the solutions are competitive, if not better, as compared to the state of the art algorithms.