Abstract
Gravitational search algorithm (GSA) is a stochastic search algorithm based on the law of gravity and mass interactions. For the purpose of enhancing the performance of standard GSA, this paper proposes a robust hybrid gravitational search algorithm (RHGSA). This algorithm makes the best of ergodicity of PieceWise Linear chaotic map to explore the global search while utilizing the sequential quadratic programming to accelerate the local search. To verify the performance of RHGSA, different types of benchmark functions including five unimodal functions and ten functions provided by CEC 2005 special session are tested in the experiments. Comparisons with other new variants of POS and GSA show that RHGSA obtains a promising performance on the majority of the test problems. Furthermore, a practical application problem, the economic load dispatch problem of power systems (ELD), is solved to further evaluate RHGSA. Compared with the previous evolutionary algorithms applied to ELD problem, RHGSA can get better results.
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