A Hybrid New Gravitational Coefficient Function of Gravitational Search Algorithm with Mutation for Search Performance

This paper proposes a hybrid New Gravitational Coefficient Function of Gravitational Search Algorithm with Mutation (NGCFGSAM). Since most of the hybrid algorithms have been concerned with the search performance of the solution. This study investigates the features that influence the algorithm on global search performance. The novel hybrid algorithm is compared to previous functions in the literature based on six benchmark functions, including both unimodal landscape functions and multimodal landscape functions. The experimental results are shown that the proposed NGCFGSAM outperforms the conventional benchmark functions. The proposed hybrid algorithm worked well on multimodal landscape functions. Better solutions compensate for the slower convergence rate by balancing the exploration and exploitation phases. For future work, studies on the investigation and rigorously prove the parameter turning for convergence rate. More benchmark functions and more algorithm comparison tests should be investigated. Disciplinary: Optimization, Engineering Management. (C) 2022 INT TRANS J ENG MANAG SCI TECH.