  In this paper, we propose an improved gravitational search algorithm named
GSABC. The algorithm improves gravitational search algorithm (GSA) results
improved by using artificial bee colony algorithm (ABC) to solve constrained
numerical optimization problems. In GSA, solutions are attracted towards each
other by applying gravitational forces, which depending on the masses assigned
to the solutions, to each other. The heaviest mass will move slower than other
masses and gravitate others. Due to nature of gravitation, GSA may pass global
minimum if some solutions stuck to local minimum. ABC updates the positions of
the best solutions that has obtained from GSA, preventing the GSA from sticking
to the local minimum by its strong searching ability. The proposed algorithm
improves the performance of GSA. The proposed method tested on 23 well-known
unimodal, multimodal and fixed-point multimodal benchmark test functions.
Experimental results show that GSABC outperforms or performs similarly to five
state-of-the-art optimization approaches.
