Privacy protection based on many‐objective optimization algorithm
Jiangjiang Zhang, Fei Xue, Xingjuan Cai, Zhihua Cui, Chang Yu, Wensheng Zhang, Wuzhao Li · Concurrency and Computation Practice and Experience · 2019
Summary It is difficult to protect users' privacy and to process private information due to the complexity and uncertainty of such information. To protect private information quickly and accurately, a many‐objective optimization algorithm framework based on the hybrid elite selection strategy is proposed in this paper. First, a mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population. Then, the balanceable fitness estimation method is employed to select and update the external archive. To test performance, the proposed algorithm is tested on many‐objective optimization problems (MaOPs) and compared with five state‐of‐the‐art algorithms. Experimental simulation results show that the proposed algorithm is more effective in solving MaOPs and can inspire development of a better privacy protection strategy.