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

Improving Gravitational Search Algorithm Performance with Artificial Bee Colony Algorithm for Constrained Numerical Optimization