Gravitation Search Algorithm with Two Masses for Constrained Optimization

In this paper, a new gravitation search algorithm with two masses is proposed to solve constrained optimization problems. In this algorithm, each particle has two masses, namely, "feasible mass" and "infeasible mass". If the particle in the feasible region is updated, then the feasible mass is used in gravitation search algorithm, otherwise, the infeasible mass is used. This idea is based on that a feasible particle is attracted towards better feasible particles and an infeasible particle is attracted towards the feasible region. Finally, the proposed algorithm is tested on 10 benchmark problems and compared with the other algorithms. The numerical results indicate that the proposed algorithm has the better performance in solving constrained optimization problems.