An Improved Gravitation Search Algorithm For Solving Constrained Optimization Problem

The global optimization problem has been widely used in many fields,and with the development of society,the problem of constrained optimization is more and more complicated,it is difficult to find the global optimal solution.In this paper,the method of solving constrained optimization problem is summarized,and its development status of advantages and disadvantages are analyzed.On this basis,two new methods to solve the problem of constrained optimization are proposed.The main work of this paper is as follows:1.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 position of a particle in the feasible region is updated,then the feasible mass is used.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 test functions and compared with the other algorithms.The numerical results indicate that the proposed algorithm has the better performance in solving constrained optimization problems.2.This paper proposes a hybrid gravitational search algorithm.By using simulated annealing penalty function method to construct a constraint problem,and then use the gravitational search algorithm to solve the constraint problem,finally through comparing with other four kinds of algorithms to proved its feasibility.