LAUSR

Gravitational search algorithm (GSA) is a novel heuristic optimization algorithm which is used to search for the global optimal solution by iteration. However, GSA is easy to fall into local minima and convergence slowly. To improve the exploration and exploitation abilities of the GSA, a new hybrid GSA (HGSA) is proposed. In this algorithm, the local search technique (LST) is incorporated into the optimization process of the GSA. For each agent, GSA is performed with probability $p$ , and LST is performed with probability $1-p$ . The probability $p$ is obtained using fuzzy logic. The strategy makes full use of the exploration ability of GSA and the exploitation ability of LST. The HGSA is tested on 23 benchmark functions. By comparing the HGSA with GSA and other algorithms that were published in recent studies, the numerical results demonstrate that the HGSA can improve the performance of GSA in terms of global optimality and solution accuracy.