Research On The Improvement Of Gravitational Search Algorithm

With the continuous progress and development of human civilization,the optimization problem is an integral part of human life.In order to solve these problems,more and more research scholars committed to optimizing the algorithm.In recent years,many new heuristic optimization algorithms have been proposed and can better solve the complex optimization problem.The Gravitational Search Algorithm(GSA)by Rashedi in2009 first proposed,is a new heuristic optimization algorithm.The improved algorithm and practical application is the main work of scholars.In order to make the optimization ability of gravitational search algorithm to achieve the best level,it requires continuous exploration and research of scholars to improve the work of the algorithm.Therefore,this paper introduces two improved methods for gravitational search algorithm,to further strengthen the optimization performance of the algorithm.The main work is as follows:1?In order to overcome the shortcomings that gravitational search algorithm(GSA)traps into local optima easily,a gravitational search algorithm with inertial mass attenuation is proposed.The attenuation rate of inertia mass is defined by membership function,and a new mutation operator is given.Finally,the proposed algorithm is tested on several benchmark test functions and compared with standard GSA and the other improved GSA.The numerical results indicate that the proposed algorithm can improve the convergence speed and precision.2?The gravitational search algorithm premature convergence,a gravitational search algorithm with adaptive mutation is proposed.In this algorithm,the Metropolis criterion of simulated annealing algorithm(SA)and hybrid mutation strategy of adaptive mutation probability is combined with the gravitational search algorithm.The numerical experiment show that the improved algorithm has better performance.