关键词: 共生生物搜索算法, 差分扰动, 自适应, 精英反向学习

Abstract: Aiming at the problems of poor convergence, low searching precision and ease of premature convergence when solving the complex optimization problems, combining with adaptive strategy, an improved SOS algorithm with different difference perturbation terms and elite opposition-base learning strategy is proposed. Experiments are conducted on the 14 benchmark functions and the results show that the improved SOS algorithm has obviously better performance in convergence speed, solution precision and global optimization than SOS algorithm and other three algorithms.

Key words: Symbiotic Organisms Search（SOS）, difference perturbation, adaptive adjustment, elite opposition-based learning