Gravitational search algorithm based on multiple adaptive constraint strategy

In order to improve the convergence speed and optimization accuracy of gravitational search algorithm, the improved gravitational algorithm with dynamically adjusting inertia weight and trend factors of speed and position is proposed. This kind of algorithm with dynamic inertia weight improves the updating way of particle mass. Moreover, the mass change has a nonlinear decreasing trend and improves the algorithm’s optimization accuracy and convergence speed. At the same time, the speed trend factor and location adaptive factor is introduced, which can dynamically constrain the moving step of each generation of particles according to the number of iterations of the current population. So the algorithm is multi-adaptive. Through classical test function and the CEC2017 benchmark function, the improved algorithm is compared and tested. The theoretical analysis proves the convergence and time complexity of the improved algorithm. Simulation results show that the improved algorithm has a remarkable improvement in terms of optimal performance, high convergence speed and optimization precision.