An adaptive disruption based gravitational search algorithm with time-varying velocity limitation

Gravitational Search Algorithm (GSA) is a swarm intelligence algorithm based on the Newtonian laws of gravity and motion. In this paper, a kind of time-varying velocity limitation strategy is presented to simulate the motion process of the masses in Standard GSA (SGSA), which is beneficial to improve convergence speed. Secondly, an adaptive disruption operator, which is based on the fitness of masses, is proposed to trade off the exploration and exploitation abilities, which help masses escape from local optima. Finally, the effectiveness and robustness of the proposed algorithm are confirmed according to the test of 23 nonlinear benchmark functions.