General Relativity Search Algorithm: A Global Optimization Approach

Abstract

In this paper, a novel metaheuristic optimization algorithm inspired by General Relativity Theory (GRT) is presented. In this method that we named General Relativity Search Algorithm (GRSA), a population of particles is considered in a space free from all external non-gravitational fields and propel toward a position with least action. Based on GRT, particles have conserved masses and move along geodesic trajectories in a curved space. Step length and step direction for updating the particles are separately computed using particles velocity and geodesics, respectively. Velocity of particles is obtained by their energy–momentums. According to physical action principle, a population of particles goes to the position with minimum action. By inspiring this physical principle, GRSA will lead variables of an optimization problem move toward the optimal point. Performance of the proposed optimization algorithm is investigated by using several standard test functions and optimal Power System Stabilizers (PSSs) design in a multi-machine power system as a real-world application. Numerical simulations results demonstrate the efficiency, robustness and convergence speed of GRSA in solving various problems.