YYPO-SA:一种新的基于 YYPO 和 SA 的混合单目标随机优化算法.

Aiming at solving the premature convergence of YYPO algorithm, this paper introduced SA strategy into the switch phase between P, and P2 in YYPO, and proposed a novel hybrid single-object optimization algorithms YYPO-SA ( including YYPO-SAl and YYPO-SA2). YYPO-SA inherited the lightweight characteristics of YYPO, and combined the YYPO' s excellent global search ability with the powerful local search performance of SA. It used the proposed 28 test functions in the single objective practical parameter algorithm competition in 2013 Evolutionary Computing Conference as the benchmark. It empirically studied the performance of YYPO-SA2, YYPO, A-YYPO, IYYPO, and 3 state of the art algorithms-GWO, WOA, and SCA based on experimental results. The experimental res ults show that YYPO-SA can perform more stable and achieve more accurate solutions. At the end, this paper also applied YYPO-SA to solve the spring design task. The experimental results show that YYPO-SA can also solve the constrained optimization task well. [ABSTRACT FROM AUTHOR]