An aggregative learning gravitational search algorithm with self-adaptive gravitational constants. (15th August 2020) - Lei, Zhenyu; Gao, Shangce; Gupta, Shubham; Cheng, Jiujun; Yang, Gang
Highlights: A new aggregative learning gravitational search algorithm is proposed. A self-adaptive gravitational constant is introduced into the algorithm. Extensive performance comparison with other state-of-the-art algorithms is done. Neural network learning tasks show the proposed algorithm's practicability. The time complexity of the proposed algorithm is analyzed. Abstract: The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence. To alleviate these issues of the GSA, an aggregative learning GSA called the ALGSA is proposed with a self-adaptive gravitational constant in which each individual possesses its own gravitational constant to improve the search performance. The proposed algorithm is compared with some existing variants of the GSA on the IEEE CEC2017 benchmark test functions to validate its search performance. Moreover, the ALGSA is also tested on neural network optimization to further verify its effectiveness. Finally, the time complexity of the ALGSA is analyzed to clarify its search performance.