An Improved Lightning Attachment Procedure Optimization Algorithm for Function Optimization
Shuang Sun, Zhiwei Ye, Yiheng Sun, Sikai Zhan, Han Yu, Quanfeng Yao · 2019
The hybridization of different meta-heuristic algorithms is for expanding the synergies of a single optimization method used alone and achieving a better optimum search performance. In this work, we proposed a hybrid optimization method combining lightning attachment procedure optimization algorithm (LAPO) and the gravitational search algorithm (GSA), and applied to the function optimization. In order to integrate the excellent exploitation performance of LAPO with the great exploration capability of the GSA to synthesize the strength of each algorithm, we utilized series hybrid mode and some benchmark test functions were employed for evaluating and comparing the performance with the standard mode. Meanwhile, the most commonly used algorithm, particle swarm optimization algorithm is also used for contrast. The experiment results show that the hybrid algorithm obtains better time efficiency and convergence capacity, also have a great ability to avoid local optimums.